Data Science Course

 

What is data science?

Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.

What is Data Science with Example?

Data science is defined as an interdisciplinary field that involves extracting knowledge and insights from data using scientific methods, algorithms, and systems. Data Science combines elements of mathematics, statistics, computer science, and domain expertise to analyze large volumes of structured and unstructured data. The goal of data science is to uncover patterns, trends, and relationships within the data to make informed decisions, solve complex problems, and create predictive models.

Define Data Science?

The term “data science” combines two key elements: “data” and “science.”

Data: It refers to the raw information that is collected, stored, and processed. In today’s digital age, enormous amounts of data are generated from various sources such as sensors, social media, transactions, and more. This data can come in structured formats (e.g., databases) or unstructured formats (e.g., text, images, videos).

Science: It refers to the systematic study and investigation of phenomena using scientific methods and principles. Science involves forming hypotheses, conducting experiments, analyzing data, and drawing conclusions based on evidence.

When we put these two elements together, “data+science” refers to the scientific study of data. Data Science involves applying scientific methods, statistical techniques, computational tools, and domain expertise to explore, analyze, and extract insights from data. The term emphasizes the rigorous and systematic approach taken to understand and derive value from vast and complex datasets.

Essentially, data science is about using scientific methods to unlock the potential of data, uncover patterns, make predictions, and drive informed decision-making across various domains and industries.

What is Data Science in Simple Words?

• Imagine you’re scrolling through your favorite social media platform, and you notice that certain types of posts always seem to grab your attention. Maybe it’s cute animal videos, delicious food recipes, or inspiring travel photos.
• Now, from the platform’s perspective, they want to keep you engaged and coming back for more. This is where data science comes into play. They collect a ton of information about what you like, share, and comment on. They use data science techniques to analyze all this information to understand your preferences better.
• For instance, they might notice that you spend more time watching animal videos than looking at food recipes. Armed with this knowledge, they can then customize your feed to show you more of what you love – adorable pets! They might even predict what type of pet video you’re likely to enjoy next based on your past behavior.
• In this scenario, data science is like the magic behind the scenes that helps social media platforms understand your interests and tailor your experience to keep you engaged. It’s all about using data to make your online experience more personalized and enjoyable.

What is Data Science Course?

A data science course is a structured educational program designed to teach individuals the foundational concepts, tools, and techniques of data science. These data science courses typically cover a wide range of topics, including statistics, programming, machine learning, data visualization, and data analysis. They are suitable for beginners with little to no prior experience in data science, as well as professionals looking to expand their skills or transition into a data-related role.

One such complete data science course which is trusted by students as well as professionals is Complete Machine Learning & Data Science Program

Key components of a data science course may include:

• Foundational Concepts: Introduction to basic concepts in data science, including data types, data manipulation, data cleaning, and exploratory data analysis.
• Programming Languages: Instruction in programming languages commonly used in data science, such as Python or R. Students learn how to write code to analyze and manipulate data, create visualizations, and build machine learning models.
• Statistical Methods: Coverage of statistical techniques and methods used in data analysis, hypothesis testing, regression analysis, and probability theory.
• Machine Learning: Introduction to machine learning algorithms, including supervised learning, unsupervised learning, and deep learning. Students learn how to apply machine learning techniques to solve real-world problems and make predictions from data.
• Data Visualization: Instruction in data visualization techniques and tools for effectively communicating insights from data. Students learn how to create plots, charts, and interactive visualizations to explore and present data.
• Practical Projects: Hands-on experience working on data science projects and case studies, where students apply their knowledge and skills to solve real-world problems and analyze real datasets.
• Capstone Project: A culminating project where students demonstrate their mastery of data science concepts and techniques by working on a comprehensive project from start to finish.

What is Data Science Job?

A data science job involves using various techniques, algorithms, and tools to extract insights and knowledge from structured and unstructured data. Here are some of the key data science job roles:

Data Scientist:

Responsibilities: Analyzing large datasets, developing machine learning models, interpreting results, and providing insights to inform business decisions.

Skills: Proficiency in programming languages like Python or R, expertise in statistics and machine learning algorithms, data visualization skills, and domain knowledge in the relevant industry.

Data Analyst:

Responsibilities: Collecting, cleaning, and analyzing data to identify trends, patterns, and insights. Often involves creating reports and dashboards to communicate findings to stakeholders.

Skills: Strong proficiency in SQL for data querying, experience with data visualization tools like Tableau or Power BI, basic statistical knowledge, and familiarity with Excel or Google Sheets.

Machine Learning Engineer:

Responsibilities: Building and deploying machine learning models at scale, optimizing model performance, and integrating them into production systems.

Skills: Proficiency in programming languages like Python or Java, experience with machine learning frameworks like TensorFlow or PyTorch, knowledge of cloud platforms like AWS or Azure, and software engineering skills for developing scalable solutions.

Data Engineer:

Responsibilities: Designing and building data pipelines to collect, transform, and store large volumes of data. Ensuring data quality, reliability, and scalability.

Skills: Expertise in database systems like SQL and NoSQL, proficiency in programming languages like Python or Java, experience with big data technologies like Hadoop or Spark, and knowledge of data warehousing concepts.

Business Intelligence (BI) Analyst:

Responsibilities: Gathering requirements from business stakeholders, designing and developing BI reports and dashboards, and providing data-driven insights to support strategic decision-making.

Skills: Proficiency in BI tools like Tableau, Power BI, or Looker, strong SQL skills for data querying, understanding of data visualization principles, and ability to translate business needs into technical solutions.

Data Architect:

Responsibilities: Designing the overall structure of data systems, including databases, data lakes, and data warehouses. Defining data models, schemas, and data governance policies.

Skills: Deep understanding of database technologies and architectures, experience with data modeling tools like ERWin or Visio, knowledge of data integration techniques, and familiarity with data security and compliance regulations.

What is Data Science Degree?

A “data science degree” refers to an academic program offered by universities or educational institutions that provides structured education and training in the field of data science. This degree program typically spans multiple years and covers a wide range of topics relevant to data analysis, machine learning, statistics, programming, and domain-specific knowledge.

A data science degree may be offered at various levels, including

• undergraduate (Bachelor’s),
• graduate (Master’s), and
• doctoral (Ph.D.) levels.

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Key Questions

What is data science?

Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.

Why is data science important?

Data science is important because it combines tools, methods, and technology to generate meaning from data. Modern organizations are inundated with data; there is a proliferation of devices that can automatically collect and store information. Online systems and payment portals capture more data in the fields of e-commerce, medicine, finance, and every other aspect of human life. We have text, audio, video, and image data available in vast quantities.  

History of data science

While the term data science is not new, the meanings and connotations have changed over time. The word first appeared in the ’60s as an alternative name for statistics. In the late ’90s, computer science professionals formalized the term. A proposed definition for data science saw it as a separate field with three aspects: data design, collection, and analysis. It still took another decade for the term to be used outside of academia. 

Future of data science

Artificial intelligence and machine learning innovations have made data processing faster and more efficient. Industry demand has created an ecosystem of courses, degrees, and job positions within the field of data science. Because of the cross-functional skillset and expertise required, data science shows strong projected growth over the coming decades.

What is data science used for?

Data science is used to study data in four main ways:

1. Descriptive analysis

Descriptive analysis examines data to gain insights into what happened or what is happening in the data environment. It is characterized by data visualizations such as pie charts, bar charts, line graphs, tables, or generated narratives. For example, a flight booking service may record data like the number of tickets booked each day. Descriptive analysis will reveal booking spikes, booking slumps, and high-performing months for this service.

2. Diagnostic analysis

Diagnostic analysis is a deep-dive or detailed data examination to understand why something happened. It is characterized by techniques such as drill-down, data discovery, data mining, and correlations. Multiple data operations and transformations may be performed on a given data set to discover unique patterns in each of these techniques.For example, the flight service might drill down on a particularly high-performing month to better understand the booking spike. This may lead to the discovery that many customers visit a particular city to attend a monthly sporting event.

3. Predictive analysis

Predictive analysis uses historical data to make accurate forecasts about data patterns that may occur in the future. It is characterized by techniques such as machine learning, forecasting, pattern matching, and predictive modeling. In each of these techniques, computers are trained to reverse engineer causality connections in the data.For example, the flight service team might use data science to predict flight booking patterns for the coming year at the start of each year. The computer program or algorithm may look at past data and predict booking spikes for certain destinations in May. Having anticipated their customer’s future travel requirements, the company could start targeted advertising for those cities from February.

4. Prescriptive analysis

Prescriptive analytics takes predictive data to the next level. It not only predicts what is likely to happen but also suggests an optimum response to that outcome. It can analyze the potential implications of different choices and recommend the best course of action. It uses graph analysis, simulation, complex event processing, neural networks, and recommendation engines from machine learning.         

Back to the flight booking example, prescriptive analysis could look at historical marketing campaigns to maximize the advantage of the upcoming booking spike. A data scientist could project booking outcomes for different levels of marketing spend on various marketing channels. These data forecasts would give the flight booking company greater confidence in their marketing decisions.

What are the benefits of data science for business?

Data science is revolutionizing the way companies operate. Many businesses, regardless of size, need a robust data science strategy to drive growth and maintain a competitive edge. Some key benefits include:

Discover unknown transformative patterns

Data science allows businesses to uncover new patterns and relationships that have the potential to transform the organization. It can reveal low-cost changes to resource management for maximum impact on profit margins.For example, an e-commerce company uses data science to discover that too many customer queries are being generated after business hours. Investigations reveal that customers are more likely to purchase if they receive a prompt response instead of an answer the next business day. By implementing 24/7 customer service, the business grows its revenue by 30%.

Innovate new products and solutions

Data science can reveal gaps and problems that would otherwise go unnoticed. Greater insight about purchase decisions, customer feedback, and business processes can drive innovation in internal operations and external solutions.For example, an online payment solution uses data science to collate and analyze customer comments about the company on social media. Analysis reveals that customers forget passwords during peak purchase periods and are unhappy with the current password retrieval system. The company can innovate a better solution and see a significant increase in customer satisfaction.

Real-time optimization

It’s very challenging for businesses, especially large-scale enterprises, to respond to changing conditions in real-time. This can cause significant losses or disruptions in business activity. Data science can help companies predict change and react optimally to different circumstances.For example, a truck-based shipping company uses data science to reduce downtime when trucks break down. They identify the routes and shift patterns that lead to faster breakdowns and tweak truck schedules. They also set up an inventory of common spare parts that need frequent replacement so trucks can be repaired faster.  

What is the data science process?

A business problem typically initiates the data science process. A data scientist will work with business stakeholders to understand what business needs. Once the problem has been defined, the data scientist may solve it using the OSEMN data science process:

O – Obtain data

Data can be pre-existing, newly acquired, or a data repository downloadable from the internet. Data scientists can extract data from internal or external databases, company CRM software, web server logs, social media or purchase it from trusted third-party sources.

S – Scrub data

Data scrubbing, or data cleaning, is the process of standardizing the data according to a predetermined format. It includes handling missing data, fixing data errors, and removing any data outliers. Some examples of data scrubbing are:· 

1. Changing all date values to a common standard format.·  
2. Fixing spelling mistakes or additional spaces.·  
3. Fixing mathematical inaccuracies or removing commas from large numbers.
4. E – Explore data

Data exploration is preliminary data analysis that is used for planning further data modeling strategies. Data scientists gain an initial understanding of the data using descriptive statistics and data visualization tools. Then they explore the data to identify interesting patterns that can be studied or actioned.      

M – Model data

Software and machine learning algorithms are used to gain deeper insights, predict outcomes, and prescribe the best course of action. Machine learning techniques like association, classification, and clustering are applied to the training data set. The model might be tested against predetermined test data to assess result accuracy. The data model can be fine-tuned many times to improve result outcomes. 

N – Interpret results

Data scientists work together with analysts and businesses to convert data insights into action. They make diagrams, graphs, and charts to represent trends and predictions. Data summarization helps stakeholders understand and implement results effectively.

What are the data science techniques?

Data science professionals use computing systems to follow the data science process. The top techniques used by data scientists are:

Classification

Classification is the sorting of data into specific groups or categories. Computers are trained to identify and sort data. Known data sets are used to build decision algorithms in a computer that quickly processes and categorizes the data. For example:·  

• Sort products as popular or not popular·  
• Sort insurance applications as high risk or low risk·  
• Sort social media comments into positive, negative, or neutral.
• Data science professionals use computing systems to follow the data science process. 

Regression

Regression is the method of finding a relationship between two seemingly unrelated data points. The connection is usually modeled around a mathematical formula and represented as a graph or curves. When the value of one data point is known, regression is used to predict the other data point. For example:·  

• The rate of spread of air-borne diseases.· 
•  The relationship between customer satisfaction and the number of employees.·  
• The relationship between the number of fire stations and the number of injuries due to fire in a particular location. 

Clustering

Clustering is the method of grouping closely related data together to look for patterns and anomalies. Clustering is different from sorting because the data cannot be accurately classified into fixed categories. Hence the data is grouped into most likely relationships. New patterns and relationships can be discovered with clustering. For example: ·  

• Group customers with similar purchase behavior for improved customer service.·  
• Group network traffic to identify daily usage patterns and identify a network attack faster.  
• Cluster articles into multiple different news categories and use this information to find fake news content.

The basic principle behind data science techniques

While the details vary, the underlying principles behind these techniques are:

• Teach a machine how to sort data based on a known data set. For example, sample keywords are given to the computer with their sort value. “Happy” is positive, while “Hate” is negative.
• Give unknown data to the machine and allow the device to sort the dataset independently.
•  Allow for result inaccuracies and handle the probability factor of the result.

What are different data science technologies?

Data science practitioners work with complex technologies such as:

• Artificial intelligence: Machine learning models and related software are used for predictive and prescriptive analysis.
• Cloud computing: Cloud technologies have given data scientists the flexibility and processing power required for advanced data analytics.
• Internet of things: IoT refers to various devices that can automatically connect to the internet. These devices collect data for data science initiatives. They generate massive data which can be used for data mining and data extraction.
• Quantum computing: Quantum computers can perform complex calculations at high speed. Skilled data scientists use them for building complex quantitative algorithms.

How does data science compare to other related data fields?

Data science is an all-encompassing term for other data-related roles and fields. Let’s look at some of them here:

What is the difference between data science and data analytics?

While the terms may be used interchangeably, data analytics is a subset of data science. Data science is an umbrella term for all aspects of data processing—from the collection to modeling to insights. On the other hand, data analytics is mainly concerned with statistics, mathematics, and statistical analysis. It focuses on only data analysis, while data science is related to the bigger picture around organizational data.In most workplaces, data scientists and data analysts work together towards common business goals. A data analyst may spend more time on routine analysis, providing regular reports. A data scientist may design the way data is stored, manipulated, and analyzed. Simply put, a data analyst makes sense out of existing data, whereas a data scientist creates new methods and tools to process data for use by analysts.

What is the difference between data science and business analytics?

While there is an overlap between data science and business analytics, the key difference is the use of technology in each field. Data scientists work more closely with data technology than business analysts.Business analysts bridge the gap between business and IT. They define business cases, collect information from stakeholders, or validate solutions. Data scientists, on the other hand, use technology to work with business data. They may write programs, apply machine learning techniques to create models, and develop new algorithms. Data scientists not only understand the problem but can also build a tool that provides solutions to the problem.It’s not unusual to find business analysts and data scientists working on the same team. Business analysts take the output from data scientists and use it to tell a story that the broader business can understand.

What is the difference between data science and data engineering?

Data engineers build and maintain the systems that allow data scientists to access and interpret data. They work more closely with underlying technology than a data scientist. The role generally involves creating data models, building data pipelines, and overseeing extract, transform, load (ETL). Depending on organization setup and size, the data engineer may also manage related infrastructure like big-data storage, streaming, and processing platforms like Amazon S3.Data scientists use the data that data engineers have processed to build and train predictive models. Data scientists may then hand over the results to the analysts for further decision making.

What is the difference between data science and machine learning?

learning?Machine learning is the science of training machines to analyze and learn from data the way humans do. It is one of the methods used in data science projects to gain automated insights from data. Machine learning engineers specialize in computing, algorithms, and coding skills specific to machine learning methods. Data scientists might use machine learning methods as a tool or work closely with other machine learning engineers to process data.

What is the difference between data science and statistics? 

Statistics is a mathematically-based field that seeks to collect and interpret quantitative data. In contrast, data science is a multidisciplinary field that uses scientific methods, processes, and systems to extract knowledge from data in various forms. Data scientists use methods from many disciplines, including statistics. However, the fields differ in their processes and the problems they study.  

What are different data science tools?

AWS has a range of tools to support data scientists around the globe:

Data storage

For data warehousing, Amazon Redshift can run complex queries against structured or unstructured data. Analysts and data scientists can use AWS Glue to manage and search for data. AWS Glue automatically creates a unified catalog of all data in the data lake, with metadata attached to make it discoverable.

Machine learning

Amazon SageMaker is a fully-managed machine learning service that runs on the Amazon Elastic Compute Cloud (EC2). It allows users to organize data, build, train and deploy machine learning models, and scale operations.

Analytics

 Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 or Glacier. It is fast, serverless, and works using standard SQL queries.

Amazon Elastic MapReduce (EMR) processes big data using servers like Spark and Hadoop.

 Amazon Kinesis allows aggregation and processing of streaming data in real-time. It uses website clickstreams, application logs, and telemetry data from IoT devices. 

Amazon OpenSearch allows search, analysis, and visualization of petabytes of data.

What does a data scientist do?

A data scientist can use a range of different techniques, tools, and technologies as part of the data science process. Based on the problem, they pick the best combinations for faster and more accurate results.

A data scientist’s role and day-to-day work vary depending on the size and requirements of the organization. While they typically follow the data science process, the details may vary. In larger data science teams, a data scientist may work with other analysts, engineers, machine learning experts, and statisticians to ensure the data science process is followed end-to-end and business goals are achieved. 

However, in smaller teams, a data scientist may wear several hats. Based on experience, skills, and educational background, they may perform multiple roles or overlapping roles. In this case, their daily responsibilities might include engineering, analysis, and machine learning along with core data science methodologies. 

What are the challenges faced by data scientists?

Multiple data sources

Different types of apps and tools generate data in various formats. Data scientists have to clean and prepare data to make it consistent. This can be tedious and time-consuming.

Understanding the business problem

Data scientists have to work with multiple stakeholders and business managers to define the problem to be solved. This can be challenging—especially in large companies with multiple teams that have varying requirements.

Elimination of bias

Machine learning tools are not completely accurate, and some uncertainty or bias can exist as a result. Biases are imbalances in the training data or prediction behavior of the model across different groups, such as age or income bracket. For instance, if the tool is trained primarily on data from middle-aged individuals, it may be less accurate when making predictions involving younger and older people. The field of machine learning provides an opportunity to address biases by detecting them and measuring them in the data and model.

How to become a data scientist?

There are usually three steps to becoming a data scientist:

1. Earn a bachelor's degree in IT, computer science, math, physics, or another related field.
2. Earn a master's degree in data science or related field.
3. Gain experience in a field of interest

What is data science?

Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI) and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning.

The accelerating volume of data sources, and subsequently data, has made data science is one of the fastest growing field across every industry. As a result, it is no surprise that the role of the data scientist was dubbed the “sexiest job of the 21st century” by Harvard Business Review (link resides outside ibm.com). Organizations are increasingly reliant on them to interpret data and provide actionable recommendations to improve business outcomes.

The data science lifecycle involves various roles, tools, and processes, which enables analysts to glean actionable insights. 

A Data Science Project undergoes the following stages:

Data ingestion: The lifecycle begins with the data collection—both raw structured and unstructured data from all relevant sources using a variety of methods. These methods can include manual entry, web scraping, and real-time streaming data from systems and devices. Data sources can include structured data, such as customer data, along with unstructured data like log files, video, audio, pictures, the Internet of Things (IoT), social media, and more.

Data storage and data processing: Since data can have different formats and structures, companies need to consider different storage systems based on the type of data that needs to be captured. Data management teams help to set standards around data storage and structure, which facilitate workflows around analytics, machine learning and deep learning models. This stage includes cleaning data, deduplicating, transforming and combining the data using ETL (extract, transform, load) jobs or other data integration technologies. This data preparation is essential for promoting data quality before loading into a data warehouse, data lake, or other repository.

Data analysis: Here, data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data. This data analytics exploration drives hypothesis generation for a/b testing. It also allows analysts to determine the data’s relevance for use within modeling efforts for predictive analytics, machine learning, and/or deep learning. Depending on a model’s accuracy, organizations can become reliant on these insights for business decision making, allowing them to drive more scalability.

Communicate: Finally, insights are presented as reports and other data visualizations that make the insights—and their impact on business—easier for business analysts and other decision-makers to understand. A data science programming language such as R or Python includes components for generating visualizations; alternately, data scientists can use dedicated visualization tools.

AI: trust in data, trust in models and trust in processes.

Data science versus Data scientist

Data science is considered a discipline, while data scientists are the practitioners within that field. Data scientists are not necessarily directly responsible for all the processes involved in the data science lifecycle. For example, data pipelines are typically handled by data engineers—but the data scientist may make recommendations about what sort of data is useful or required. While data scientists can build machine learning models, scaling these efforts at a larger level requires more software engineering skills to optimize a program to run more quickly. As a result, it’s common for a data scientist to partner with machine learning engineers to scale machine learning models.

Data scientist responsibilities can commonly overlap with a data analyst, particularly with exploratory data analysis and data visualization. However, a data scientist’s skillset is typically broader than the average data analyst. Comparatively speaking, data scientist leverage common programming languages, such as R and Python, to conduct more statistical inference and data visualization.

To perform these tasks, data scientists require computer science and pure science skills beyond those of a typical business analyst or data analyst. The data scientist must also understand the specifics of the business, such as automobile manufacturing, eCommerce, or healthcare.

In short, a data scientist must be able to:

Know enough about the business to ask pertinent questions and identify business pain points. Apply statistics and computer science, along with business acumen, to data analysis. Use a wide range of tools and techniques for preparing and extracting data—everything from databases and SQL to data mining to data integration methods.

Extract insights from big data using predictive analytics and artificial intelligence (AI), including machine learning models, natural language processing, and deep learning.

Write programs that automate data processing and calculations.

Tell—and illustrate—stories that clearly convey the meaning of results to decision-makers and stakeholders at every level of technical understanding.

Explain how the results can be used to solve business problems.

Collaborate with other data science team members, such as data and business analysts, IT architects, data engineers, and application developers. These skills are in high demand, and as a result, many individuals that are breaking into a data science career, explore a variety of data science programs, such as certification programs, data science courses, and degree programs offered by educational institutions.

Data science versus business intelligence

It may be easy to confuse the terms “data science” and “business intelligence” (BI) because they both relate to an organization’s data and analysis of that data, but they do differ in focus.

Business intelligence (BI) is typically an umbrella term for the technology that enables data preparation, data mining, data management, and data visualization. Business intelligence tools and processes allow end users to identify actionable information from raw data, facilitating data-driven decision-making within organizations across various industries. While data science tools overlap in much of this regard, business intelligence focuses more on data from the past, and the insights from BI tools are more descriptive in nature. It uses data to understand what happened before to inform a course of action. BI is geared toward static (unchanging) data that is usually structured. While data science uses descriptive data, it typically utilizes it to determine predictive variables, which are then used to categorize data or to make forecasts.

Data science and BI are not mutually exclusive—digitally savvy organizations use both to fully understand and extract value from their data.

Data science tools

Data scientists rely on popular programming languages to conduct exploratory data analysis and statistical regression. These open source tools support pre-built statistical modeling, machine learning, and graphics capabilities. These languages include the following (read more at "Python vs. R: What's the Difference?"):

R Studio: An open source programming language and environment for developing statistical computing and graphics.

Python: It is a dynamic and flexible programming language. The Python includes numerous libraries, such as NumPy, Pandas, Matplotlib, for analyzing data quickly.

To facilitate sharing code and other information, data scientists may use GitHub and Jupyter notebooks.

Some data scientists may prefer a user interface, and two common enterprise tools for statistical analysis include:

SAS: A comprehensive tool suite, including visualizations and interactive dashboards, for analyzing, reporting, data mining, and predictive modeling.

IBM SPSS: Offers advanced statistical analysis, a large library of machine learning algorithms, text analysis, open source extensibility, integration with big data, and seamless deployment into applications.

Data scientists also gain proficiency in using big data processing platforms, such as Apache Spark, the open source framework Apache Hadoop, and NoSQL databases. They are also skilled with a wide range of data visualization tools, including simple graphics tools included with business presentation and spreadsheet applications (like Microsoft Excel), built-for-purpose commercial visualization tools like Tableau and IBM Cognos, and open source tools like D3.js (a JavaScript library for creating interactive data visualizations) and RAW Graphs. For building machine learning models, data scientists frequently turn to several frameworks like PyTorch, TensorFlow, MXNet, and Spark MLib.

Given the steep learning curve in data science, many companies are seeking to accelerate their return on investment for AI projects; they often struggle to hire the talent needed to realize data science project’s full potential. To address this gap, they are turning to multipersona data science and machine learning (DSML) platforms, giving rise to the role of “citizen data scientist.”

Multipersona DSML platforms use automation, self-service portals, and low-code/no-code user interfaces so that people with little or no background in digital technology or expert data science can create business value using data science and machine learning. These platforms also support expert data scientists by also offering a more technical interface. Using a multipersona DSML platform encourages collaboration across the enterprise.

Data science and cloud computing

Cloud computing scales data science by providing access to additional processing power, storage, and other tools required for data science projects.

Since data science frequently leverages large data sets, tools that can scale with the size of the data is incredibly important, particularly for time-sensitive projects. Cloud storage solutions, such as data lakes, provide access to storage infrastructure, which are capable of ingesting and processing large volumes of data with ease. These storage systems provide flexibility to end users, allowing them to spin up large clusters as needed. They can also add incremental compute nodes to expedite data processing jobs, allowing the business to make short-term tradeoffs for a larger long-term outcome. Cloud platforms typically have different pricing models, such a per-use or subscriptions, to meet the needs of their end user—whether they are a large enterprise or a small startup.

Open source technologies are widely used in data science tool sets. When they’re hosted in the cloud, teams don’t need to install, configure, maintain, or update them locally. Several cloud providers, including IBM Cloud®, also offer prepackaged tool kits that enable data scientists to build models without coding, further democratizing access to technology innovations and data insights. 

Data science use cases

Enterprises can unlock numerous benefits from data science. Common use cases include process optimization through intelligent automation and enhanced targeting and personalization to improve the customer experience (CX). However, more specific examples include:

Here are a few representative use cases for data science and artificial intelligence:

• An international bank delivers faster loan services with a mobile app using machine learning-powered credit risk models and a hybrid cloud computing architecture that is both powerful and secure.
• An electronics firm is developing ultra-powerful 3D-printed sensors to guide tomorrow’s driverless vehicles. The solution relies on data science and analytics tools to enhance its real-time object detection capabilities.
• A robotic process automation (RPA) solution provider developed a cognitive business process mining solution that reduces incident handling times between 15% and 95% for its client companies. The solution is trained to understand the content and sentiment of customer emails, directing service teams to prioritize those that are most relevant and urgent.
• A digital media technology company created an audience analytics platform that enables its clients to see what’s engaging TV audiences as they’re offered a growing range of digital channels. The solution employs deep analytics and machine learning to gather real-time insights into viewer behavior.
• An urban police department created statistical incident analysis tools to help officers understand when and where to deploy resources in order to prevent crime. The data-driven solution creates reports and dashboards to augment situational awareness for field officers.
• Shanghai Changjiang Science and Technology Development used IBM® Watson® technology to build an AI-based medical assessment platform that can analyze existing medical records to categorize patients based on their risk of experiencing a stroke and that can predict the success rate of different treatment plans.

What Is Data Science?

Data science is an essential part of many industries today, given the massive amounts of data that are produced, and is one of the most debated topics in IT circles. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In this article, we’ll learn what data science is, and how you can become a data scientist.

What Is Data Science?

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques, including essential data science skills, to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models. The data used for analysis can come from many different sources and presented in various formats. Now that you know what data science is, let’s see the data science lifestyle.

The Data Science Lifecycle

Now that you know what is data science, next up let us focus on the data science lifecycle. Data science’s lifecycle consists of five distinct stages, each with its own tasks:

Capture: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.

Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture. This stage covers taking the raw data and putting it in a form that can be used.

Process: Data Mining, Clustering/Classification, Data Modeling, Data Summarization. Data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis.

Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. 

Here is the real meat of the lifecycle. This stage involves performing the various analyses on the data.

Communicate: Data Reporting, Data Visualization, Business Intelligence, Decision Making. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.

Become a Data Scientist With Real-World Experience

Data Science Prerequisites

Here are some of the technical concepts you should know about before starting to learn what is data science.

1. Machine Learning: Machine learning is the backbone of data science. Data Scientists need to have a solid grasp of ML in addition to basic knowledge of statistics.

2. Modeling: Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of Machine Learning and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.

3. Statistics: Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.

4. Programming: Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.

5. Database: A capable data scientist needs to understand how databases work, how to manage them, and how to extract data from them.

Who Oversees the Data Science Process?

1. Business Managers: The business managers are the people in charge of overseeing the data science training method. Their primary responsibility is to collaborate with the data science team to characterise the problem and establish an analytical method. A data scientist may oversee the marketing, finance, or sales department, and report to an executive in charge of the department. Their goal is to ensure projects are completed on time by collaborating closely with data scientists and IT managers.

2. IT Managers: Following them are the IT managers. If the member has been with the organisation for a long time, the responsibilities will undoubtedly be more important than any others. They are primarily responsible for developing the infrastructure and architecture to enable data science activities. Data science teams are constantly monitored and resourced accordingly to ensure that they operate efficiently and safely. They may also be in charge of creating and maintaining IT environments for data science teams.

3. Data Science Managers: The data science managers make up the final section of the tea. They primarily trace and supervise the working procedures of all data science team members. They also manage and keep track of the day-to-day activities of the three data science teams. They are team builders who can blend project planning and monitoring with team growth.

What is a Data Scientist?

If learning what is data science sounded interesting, understanding what does this job roles is all about will me much more interesting to you. Data scientists are among the most recent analytical data professionals who have the technical ability to handle complicated issues as well as the desire to investigate what questions need to be answered. They're a mix of mathematicians, computer scientists, and trend forecasters. They're also in high demand and well-paid because they work in both the business and IT sectors. On a daily basis, a data scientist may do the following tasks:

Discover patterns and trends in datasets to get insights

Create forecasting algorithms and data models

Improve the quality of data or product offerings by utilizing machine learning techniques

Distribute suggestions to other teams and top management

In data analysis, use data tools such as R, SAS, Python, or SQL

Top the field of data science innovations

What Does a Data Scientist Do?

You know what is data science, and you must be wondering what exactly is this job role like - here's the answer. A data scientist analyzes business data to extract meaningful insights. In other words, a data scientist solves business problems through a series of steps, including:

Before tackling the data collection and analysis, the data scientist determines the problem by asking the right questions and gaining understanding.

The data scientist then determines the correct set of variables and data sets.

The data scientist gathers structured and unstructured data from many disparate sources—enterprise data, public data, etc.

Once the data is collected, the data scientist processes the raw data and converts it into a format suitable for analysis. This involves cleaning and validating the data to guarantee uniformity, completeness, and accuracy.

After the data has been rendered into a usable form, it’s fed into the analytic system—ML algorithm or a statistical model. This is where the data scientists analyze and identify patterns and trends.

When the data has been completely rendered, the data scientist interprets the data to find opportunities and solutions.

The data scientists finish the task by preparing the results and insights to share with the appropriate stakeholders and communicating the results.

Why Become a Data Scientist?

You learnt what is data science. Did it sound exciting? Here's another solid reason why you should pursue data science as your work-field. According to Glassdoor and Forbes, demand for data scientists will increase by 28 percent by 2026, which speaks of the profession’s durability and longevity, so if you want a secure career, data science offers you that chance. So, if you’re looking for an exciting career that offers stability and generous compensation, then look no further!

Uses of Data Science

Data science may detect patterns in seemingly unstructured or unconnected data, allowing conclusions and predictions to be made.

Tech businesses that acquire user data can utilise strategies to transform that data into valuable or profitable information.

Data Science has also made inroads into the transportation industry, such as with driverless cars. It is simple to lower the number of accidents with the use of driverless cars. For example, with driverless cars, training data is supplied to the algorithm, and the data is examined using data Science approaches, such as the speed limit on the highway, busy streets, etc.

Data Science applications provide a better level of therapeutic customisation through genetics and genomics research.

Now that you know the uses of Data Science and what is data science in general, let's see all the opportunity that this feild offers to focus on and specialize in one aspect of the field. Here’s a sample of different ways you can fit into this exciting, fast-growing field.

Data Scientist

Job role: Determine what the problem is, what questions need answers, and where to find the data. Also, they mine, clean, and present the relevant data.

Skills needed: Programming skills (SAS, R, Python), storytelling and data visualization, statistical and mathematical skills, knowledge of Hadoop, SQL, and Machine Learning.

Data Analyst

Job role: Analysts bridge the gap between the data scientists and the business analysts, organizing and analyzing data to answer the questions the organization poses. They take the technical analyses and turn them into qualitative action items.

Skills needed: Statistical and mathematical skills, programming skills (SAS, R, Python), plus experience in data wrangling and data visualization.

Data Engineer

Job role: Data engineers focus on developing, deploying, managing, and optimizing the organization’s data infrastructure and data pipelines. Engineers support data scientists by helping to transfer and transform data for queries.

Skills needed: NoSQL databases (e.g., MongoDB, Cassandra DB), programming languages such as Java and Scala, and frameworks (Apache Hadoop).

Data Science Tools

The data science profession is challenging, but fortunately, there are plenty of tools available to help the data scientist succeed at their job. And now that we know what is data science, it's lifecycle and more about the role in general, let us dig into it's tools.

Applications of Data Science

There are various applications of data science, including:

1. Healthcare

Healthcare companies are using data science to build sophisticated medical instruments to detect and cure diseases.

2. Gaming

Video and computer games are now being created with the help of data science and that has taken the gaming experience to the next level.

3. Image Recognition

Identifying patterns is one of the most commonly known applications of data science. in images and detecting objects in an image is one of the most popular data science applications.

4. Recommendation Systems

Next up in the data science and its applications list comes Recommendation Systems. Netflix and Amazon give movie and product recommendations based on what you like to watch, purchase, or browse on their platforms.

5. Logistics

Data Science is used by logistics companies to optimize routes to ensure faster delivery of products and increase operational efficiency.

6. Fraud Detection

Fraud detection comes the next in the list of applications of data science. Banking and financial institutions use data science and related algorithms to detect fraudulent transactions.   

7. Internet Search

Internet comes the next in the list of applications of data science. When we think of search, we immediately think of Google. Right? However, there are other search engines, such as Yahoo, Duckduckgo, Bing, AOL, Ask, and others, that employ data science algorithms to offer the best results for our searched query in a matter of seconds. Given that Google handles more than 20 petabytes of data per day. Google would not be the 'Google' we know today if data science did not exist.

8. Speech recognition

Speech recognition is one of the most commonly known applications of data science. It is a technology that enables a computer to recognize and transcribe spoken language into text. It has a wide range of applications, from virtual assistants and voice-controlled devices to automated customer service systems and transcription services.

9. Targeted Advertising

If you thought Search was the most essential data science use, consider this: the whole digital marketing spectrum. From display banners on various websites to digital billboards at airports, data science algorithms are utilised to identify almost anything. This is why digital advertisements have a far higher CTR (Call-Through Rate) than traditional marketing. They can be customised based on a user's prior behaviour. That is why you may see adverts for Data Science Training Programs while another person sees an advertisement for clothes in the same region at the same time.

10. Airline Route Planning

Next up in the data science and its applications list comes route planning. As a result of data science, it is easier to predict flight delays for the airline industry, which is helping it grow. It also helps to determine whether to land immediately at the destination or to make a stop in between, such as a flight from Delhi to the United States of America or to stop in between and then arrive at the destination.

11. Augmented Reality

Last but not least, the final data science applications appear to be the most fascinating in the future. Yes, we are discussing something other than augmented reality. Do you realise there's a fascinating relationship between data science and virtual reality? A virtual reality headset incorporates computer expertise, algorithms, and data to create the greatest viewing experience possible. The popular game Pokemon GO is a minor step in that direction. The ability to wander about and look at Pokemon on walls, streets, and other non-existent surfaces. The makers of this game chose the locations of the Pokemon and gyms using data from Ingress, the previous app from the same business.

Here are some brief example of data science showing data science’s versatility.

Law Enforcement: In this scenario, data science is used to help police in Belgium to better understand where and when to deploy personnel to prevent crime. With only limited resources and a large area to cover data science used dashboards and reports to increase the officers’ situational awareness, allowing a police force that’s spread thin to maintain order and anticipate criminal activity.

Pandemic Fighting: The state of Rhode Island wanted to reopen schools, but was naturally cautious, considering the ongoing COVID-19 pandemic. The state used data science to expedite case investigations and contact tracing, enabling a small staff to handle an overwhelming number of concerned calls from citizens. This information helped the state set up a call center and coordinate preventative measures.

Driverless Vehicles: Lunewave, a sensor manufacturing company, was looking for a way to make sensor technology more cost-effective and accurate. They turned to data science and machine learning to train their sensors to be safer and more reliable, as well as using data to improve their 3D-printed sensor manufacturing process.

FAQs

1. What is data science in simple words?

Data science, in simple words, is the field of study that involves collecting, analyzing, and interpreting large sets of data to uncover insights, patterns, and trends that can be used to make informed decisions and solve real-world problems.

2. What is data science used for?

Data science is used for a wide range of applications, including predictive analytics, machine learning, data visualization, recommendation systems, fraud detection, sentiment analysis, and decision-making in various industries like healthcare, finance, marketing, and technology.

3. What’s the difference between data science, artificial intelligence, and machine learning?

Artificial Intelligence makes a computer act/think like a human. Data science is an AI subset that deals with data methods, scientific analysis, and statistics, all used to gain insight and meaning from data. Machine learning is a subset of AI that teaches computers to learn things from provided data.

4. What does a Data Scientist do?

A data scientist analyzes business data to extract meaningful insights.

5. What kinds of problems do data scientists solve?

Data scientists solve issues like:

Loan risk mitigation

Pandemic trajectories and contagion patterns

Effectiveness of various types of online advertisement

Resource allocation

6. Do data scientists code?

Sometimes they may be called upon to do so.

7. What is the data science course eligibility?

If you wish to know anything about our data science course, please check out Data Science Bootcamp and Data Science master’s program.

8. Can I learn Data Science on my own?

Data science is a complex field with many difficult technical requirements. It’s not advisable to try learning data science without the help of a structured learning program.

Wrapping It All Up

Data will be the lifeblood of the business world for the foreseeable future. Knowledge is power, and data is actionable knowledge that can mean the difference between corporate success and failure. By incorporating data science techniques into their business, companies can now forecast future growth, predict potential problems, and devise informed strategies for success. This is the perfect time for you to start your career in data science.

Do you have any questions regarding this ‘What is Data Science’ article? If so, then please put it in the comments section of the article. Our team will help you solve your queries at the earliest.