Data Science Course Syllabus – The Detailed Guide

A data scientist’s role is key in today’s data-driven world. These professionals analyze large volumes of data using statistical methods, machine learning, and data visualization and help businesses uncover valuable insights. These insights allow companies to make smarter decisions, improving their operations and strategies. 

Additionally, the demand for data scientists has skyrocketed by 650%, making it one of the fastest-growing fields. If you want to pursue a career in data science, choosing the right job-oriented course is crucial. In this blog, we’ll explore the essential topics covered in a typical data science course syllabus, helping you make an informed choice. 

What is Data Science: An Overview

Data science is a field that combines different techniques like statistics, machine learning, and data mining to make sense of both organized and unorganized data. It helps solve difficult business problems, improves decision-making, and brings new ideas to life in various industries. 

If you’re interested in a career in data science, enrolling in a data science course with a placement guarantee can be a smart choice. Such a course will help you gain important skills and also help you in securing a job after completion of the course. 

What are the Prerequisites of a Data Science Course? 

Anyone having an interest in learning about data science can pursue a professional course. It demands a prerequisite for taking the course. The prerequisites for a data science course can vary depending on the level of the program (beginner, intermediate, or advanced). However, you should have an aptitude for quantitative and analytical skills. 

Before we explore the data science course syllabus, let us understand the prerequisites you need to complete the course:

• Mathematical Skills: A solid understanding of statistics, probability, and linear algebra is essential for developing data science algorithms and manipulating data effectively.

• Programming Knowledge: Familiarity with programming, especially Python, is beneficial due to its extensive use in data science. Knowing R or SQL is also important for data manipulation.

• Excel Proficiency: Proficiency in MS Excel is helpful for data cleaning, analysis, and basic visualization, laying a foundation for more advanced tools.

• Problem-Solving Skills: Strong problem-solving, critical thinking, and logical reasoning skills are crucial for identifying problems and deriving insights from data.

• Database Understanding: Basic knowledge of databases and SQL is important for working with large datasets, enabling efficient data retrieval and preparation.

• Data Visualization Tools: While not mandatory for beginners, familiarity with tools like Power BI or Tableau can provide an edge in visualizing data insights.

Data Science Course Syllabus Breakdown

The data science course syllabus covers fundamentals and advanced topics. You will learn the tools and technologies required to become a professional and kickstart your career. This section covers an overview of the syllabus and the different topics included in the program:

1. Introduction to Data Analytics

Data analytics is one of the foundational topics the data science course covers. Here are the topics the data analytics module will cover:

i. Introduction to Analytics

This module will help you understand the basics of data analytics and how businesses use data to make smarter decisions. You’ll learn about three main types of analytics: descriptive, predictive, and prescriptive. These methods are key in shaping modern business strategies, helping organizations understand past data, forecast future outcomes, and recommend actions.

ii. Business Analytics

In this module, you will focus on how data-driven insights can improve business operations, engage customers more effectively, and boost profitability. You will see how analytics is applied in different industries to solve challenging problems and drive growth.

iii. Extracting Data Using Industry Standards

Here, you’ll learn how to gather and extract data effectively. Using tools like SQL and APIs, you will understand how to collect data with accuracy and integrity. This is important before any analysis, ensuring the data is reliable and useful.

Pro Tip: Uncover the data analytics roadmap to deepen your knowledge and understanding.

2. Data Management and Analysis with MS Excel

MS Excel is an important tool in data analysis and management. The module will cover the following topics:

i. Getting Started with Excel

This module is perfect for beginners who want to learn the basics of Excel. You will learn how to organize, edit, and format data, as well as use simple tools to manage datasets. It’s a great starting point for anyone new to Excel and looking to build foundational skills.

Pro Tip: Check out our guide to explore the Excel shortcut keys and make the best use of the tool in data science.

ii. All about Functions and Formulas

In this module, you’ll dive into some of Excel’s most powerful functions and formulas, such as the VLOOKUP formula, INDEX-MATCH, and IF statements. These tools are essential for complex calculations and data manipulation, making your work more efficient and accurate.

Pro Tip: Explore the guide to Excel formulas and learn them with the help of examples. 

iii. Analyse and Summarise Data

Here, you’ll learn how to create concise summaries of large datasets using PivotTables and charts. This will help you transform raw data into meaningful insights, making trends and patterns easier to understand and act on.

iv. Explore More Features in Excel

This module introduces you to more advanced features in Excel, like data validation, what-if analysis, and conditional formatting. These tools will help you better understand your data and make more informed decisions.

v. Automate with Excel

Learn how to save time by automating repetitive tasks in Excel. This module will guide you through recording macros and writing VBA scripts, enabling you to unlock Excel’s automation capabilities and streamline your workflow.

3. Data Visualization with Power BI

In this module, you will learn how to use Power BI to create compelling, interactive visualizations that bring your data to life. You will gain the skills to design insightful dashboards and reports, empowering you to communicate complex data trends clearly and effectively. Here are the topics that will be covered in this module of the course: 

i. Understanding the Basics of Power BI

In this module, you’ll learn the basics of Power BI, including its interface and features. You will explore how to build dashboards and reports that effectively communicate data insights. This is an essential starting point for understanding how Power BI helps in visualizing data.

ii. Data Preparation and Data Model Fundamentals

This module will teach you how to bring data into Power BI, clean it, and structure it for analysis. You will learn to create a coherent data model by connecting different datasets, ensuring data is well-organized and ready for deeper analysis.

iii. Manipulate Data, Create Model and Visualization

Here, you will experience the power of Power BI’s data modeling capabilities. You will see how raw data can be transformed into valuable insights using visualizations such as charts, graphs, and maps. This module will show you how to turn data into meaningful, visual stories.

iv. Intermediate Data Transformation and Visualization

In this module, you will take your skills to the next level by working with more complex data. You will create interactive reports that allow users to explore the data dynamically, making it easier for businesses to draw insights from large datasets.

v. Advanced-Data Transformation and Visualization

This advanced module will help you handle more complex data scenarios. You will dive into advanced transformation techniques, time-series analysis, and real-time data visualization, enabling you to deliver high-impact insights that can influence strategic decisions.

vi. All About Dax

Data Analysis Expressions (DAX) is a powerful formula language in Power BI. In this module, you’ll learn how to use DAX to create custom metrics, Key Performance Indicators (KPIs), and advanced calculations that will make your reports more detailed and insightful.

Pro Tip: When working with DAX in Power BI, master the basic functions like SUM, AVERAGE, and COUNT. Then progress to advanced functions like CALCULATE and FILTER to create dynamic, customized metrics for deeper insights.

vii. Working on The Cloud

This module will teach you how to use Power BI’s cloud capabilities to share reports, collaborate with others, and deliver real-time data updates. You’ll understand how cloud-based Power BI can improve teamwork and allow for real-time decision-making across your organization.

Pro Tip: Take full advantage of Power BI’s cloud capabilities by organizing reports into workspaces. This helps streamline collaboration and ensures the right people have access to the right data, making teamwork more efficient and impactful.

4. SQL for Data Analysis and Insights

In this module, you will learn how to use the power of SQL to manage, analyze, and extract valuable insights from data. You will master essential querying techniques to manipulate large datasets and derive actionable conclusions. Here are the topics that will be covered in this module of the course: 

i. Introduction to Database and SQL

In this section, you’ll explore the basics of databases and the role they play in data storage. Databases are used to store, organize, and manage large amounts of data. SQL, or Structured Query Language, is crucial because it allows you to interact with this data. You can use SQL to retrieve, update, and manipulate data stored in relational databases, which are the most common type of databases used today.

ii. Introduction to SQL

Once you’re familiar with databases, you’ll start learning SQL by writing simple queries. The SELECT statement is one of the first things you’ll learn. It helps you retrieve specific data from a database. You’ll also use the WHERE clause to filter this data, showing only what is relevant to your query. Additionally, you’ll learn how to sort your data results to make them more readable.

Pro Tip: Check out our guide on SQL commands and master the core commands like SELECT, WHERE, ORDER BY, JOIN, GROUP BY, and HAVING. Learn to combine data from multiple tables and perform more advanced analyses.

iii. Intermediate SQL

After mastering the basics, you’ll move on to more advanced operations. This includes learning JOIN operations, which allow you to combine data from multiple tables. You’ll also use the GROUP BY clause to summarize your data. Lastly, the HAVING clause helps filter summarized data, adding a layer of control to your queries.

iv. Advanced SQL

In this section, you’ll take your SQL skills to the next level. You’ll learn how to write complex queries using subqueries, which allow you to perform multiple operations within a single query. Window functions will enable you to analyze data in a detailed way by processing rows related to the current row. 

5. Python Data Preparation & Analysis

This section will guide you through the essentials of learning Python, one of the most powerful and widely used programming languages in data science. You will build a strong foundation in Python, equipping skills to tackle various data-driven tasks. Here are the topics that will be covered in this module of the course: 

i. Introduction to Python

In this section, you’ll learn Python’s basic syntax and understand why it is a popular choice for data analytics. Python is known for being easy to write and read, which makes it ideal for beginners. Its flexibility and wide range of uses, especially in data analysis, are some of the key reasons why Python is so widely adopted.

Pro Tip: Explore the Python cheat sheet to familiarize yourself with essential syntax, functions, and libraries. It’s an invaluable resource for beginners and advanced learners, helping you write cleaner and more efficient code.

ii. Flow Control

Next, you’ll dive into flow control, learning how to manage the flow of your code using loops, conditionals, and other control structures. These tools will help you handle data more effectively by allowing your programs to make decisions and repeat actions and key concepts when processing data.

iii. Data Structures and Functions

This section will focus on Python’s built-in data structures such as lists, dictionaries, and tuples. These structures are essential for organizing and manipulating data efficiently. You will also learn how to create reusable functions to make the code cleaner and more manageable, especially when working on larger projects.

iv. Error and File Handling

Error and file handling are critical for working with real-world data. You will learn how to manage exceptions and errors in your code. Additionally, you will practice reading from and writing to files, which is crucial for loading datasets and saving results.

v. Python Libraries

Python libraries such as NumPy, pandas, and Matplotlib are essential for data analysis. In this section, you’ll learn how to use these libraries for numerical data manipulation, statistical analysis, and visualizing data, helping you derive insights from your datasets.

vi. Introduction to Statistics

This section introduces key statistical concepts such as mean, median, and mode, as well as measures of spread-like variance. These concepts are vital for summarizing and interpreting data.

vii. Methods to Find Centralised Values

Here, you’ll explore measures of central tendency and dispersion, essential tools for summarizing your data. They provide quick insights into your dataset’s general patterns.

viii. Inferential Statistics

Finally, you’ll learn inferential statistics, which helps you make predictions based on sample data. You will explore hypothesis testing and confidence intervals, essential for making data-driven decisions.

6. Data-Driven- Machine Learning

Here are the topics that will be covered in this module of the course: 

i. Introduction to Machine Learning

In this section, you’ll explore the fundamental concepts of machine learning. Machine learning allows computers to learn from data without being explicitly programmed. These models can identify patterns and make predictions based on historical data, which is useful in various fields like finance, healthcare, and marketing. learn from data and can be used in prediction or pattern discovery.

ii. Supervised Machine Learning

Supervised learning is a common machine learning technique where models are trained on labeled data. This means the algorithm learns from input-output pairs and can predict outcomes for new data. You’ll learn popular algorithms like linear regression for predicting continuous values, decision trees for classification, and support vector machines, which are widely used for both regression and classification tasks.

iii. Unsupervised Machine Learning

In contrast to supervised learning, unsupervised learning deals with unlabeled data. The goal here is to discover hidden patterns and structures in the data. You will learn techniques like clustering, where algorithms such as K-Means group similar data points together. 

7. NLP Essentials: Applications to Real-World Problems

Here are the topics that will be covered in this module of the course: 

i. Introduction to Natural Language Processing

Natural Language Processing (NLP) is a key part of data science. It focuses on how computers understand and analyze human language. In this section, you’ll learn the basics of NLP, including its importance in text analysis and interpretation. 

ii. Understanding of Natural Language Processing

After grasping the basics, you’ll explore specific techniques used in NLP. You’ll study tokenization, which breaks text into smaller parts, and sentiment analysis, which determines the emotion behind the words. 

iii. Advance NLP

Once you have a solid foundation, it’s time to delve into advanced topics. Here, you will investigate complex NLP models, particularly transformers. Transformers are powerful tools that can handle intricate tasks like text generation and question-answering. 

8. Deep Learning Mastery: Data-Intensive

Here are the topics that will be covered in this module of the course: 

i. Introduction to Deep Learning

Deep learning is a powerful branch of artificial intelligence that focuses on using neural networks. In this section, you will gain a general understanding of what deep learning is and how it works. You’ll explore various applications, such as speech recognition and natural language processing, highlighting its impact across different industries. 

ii. From Neural Networks to CNNs

After grasping the basics, you’ll dive deeper into neural networks and learn about Convolutional Neural Networks (CNNs). CNNs are specialized for processing grid-like data, such as images. You’ll understand how these networks work, focusing on their unique layers and operations. 

Exciting Data Science Projects 

Here are the projects that you will have to complete during this data science course syllabus:

J P Morgan Classification for Legal Documents

In this project, you will explore how J.P. Morgan’s COIN system uses AI to streamline legal document management. COIN can quickly interpret complex legal documents, such as commercial loan agreements, reducing manual workload and minimizing errors. By automating these routine tasks, professionals can focus on more valuable work. This project will show you how AI can transform industries by making processes faster, more accurate, and more efficient.

KPMG Data Analysis using Excel

This project offers insights into customer demographics, transactions, and new customer data to evaluate business performance and customer behavior. You will complete six tasks involving data cleaning, analysis, and visualization in Excel. By the end, you will have a better understanding of how to leverage Excel for meaningful data analysis that can inform business strategies.

Airline Performance Analysis using Power BI

In this project, you will analyze and visualize airline data to gain insights into flight operations, passenger management, and ticketing. Utilizing various features of Power BI, you’ll transform and visualize the data to create a dashboard that aids in decision-making. This project will help you understand how data visualization tools can enhance operational efficiency and improve airline performance.

Optimizing Retail Insights: A Walmart Case Study

This project involves designing and analyzing Walmart’s retail operations across multiple cities using advanced SQL techniques. By examining transaction data alongside customer demographics and payment methods, you will uncover insights into sales performance and customer behavior. The analysis aims to enhance strategic decision-making and optimize business processes, ultimately improving operational efficiency.

Electric Vehicles Market Size Analysis using Python

In this project, you will use Python to analyze the market size of electric vehicles. You will gather data, clean it, and conduct analyses to understand trends and growth potential in the electric vehicle industry. This project will provide valuable experience in data handling and market analysis, showcasing how Python can be used to draw meaningful insights.

Smart Pricing & Retention: Data-Driven Solutions for Airbnb and Telecom Industries

This project is divided into the following parts: 

• Airbnb Price Prediction

In this part of the project, you will develop a model to predict Airbnb rental prices based on various factors, such as location, amenities, and seasonality. You will use statistical techniques and machine learning algorithms to provide accurate pricing recommendations.

• Customer Churn Prediction

Here, you will analyze customer data from telecom industries to predict churn. By identifying patterns and factors that lead to customer loss, you can suggest strategies for retention. This project will highlight the importance of data in making informed business decisions.

Text Intelligence: Sentiment Analysis of IMDb Reviews and News Article Classification

In this project, you will perform sentiment analysis on IMDb movie reviews to understand audience opinions and trends. You will also classify news articles based on their content. Using natural language processing techniques, you’ll gain insights into how people feel about different movies and how to categorize news information effectively. 

What are the Prerequisites of a Data Science Course?

Now that you’re familiar with the data science course syllabus, it’s essential to understand the prerequisites needed for the course. Below are the requirements you need to fulfill to pursue a data science program:

• Mathematical Skills: A solid understanding of statistics, probability, and linear algebra is essential for developing data science algorithms and manipulating data effectively.

• Programming Knowledge: Familiarity with programming, especially Python, is beneficial due to its extensive use in data science. Knowing R or SQL is also important for data manipulation.

• Excel Proficiency: Proficiency in MS Excel is helpful for data cleaning, analysis, and basic visualization, laying a foundation for more advanced tools.

• Problem-Solving Skills: Strong problem-solving, critical thinking, and logical reasoning skills are crucial for identifying problems and deriving insights from data.

• Database Understanding: Basic knowledge of databases and SQL is important for working with large datasets, enabling efficient data retrieval and preparation.

• Data Visualization Tools: While not mandatory for beginners, familiarity with tools like Power BI or Tableau can provide an edge in visualizing data insights.

Skills that You Can Develop From This Data Science Course

By completing the data science course, you will build a strong foundation and advanced knowledge in essential areas and gain hands-on experience with industry-leading tools and techniques. The following list highlights the critical skills you will master as you progress through the data science course syllabus. These skills will prepare you to tackle real-world challenges in data analysis, machine learning, and beyond:

• Data Manipulation and Analysis: This course teaches one how to clean, transform, and analyze large datasets using tools like Python, R, and SQL are key to extracting meaningful insights.
• Statistical Analysis: The course teaches the core statistical concepts, which enable one to apply techniques involving regression, hypothesis testing, and probability models toward making data-driven decisions.
• Machine Learning: Supervised and unsupervised machine learning algorithms will be introduced so that you form predictive models solving rather complex problems.
• Data Visualization: You will be effective in communicating insights to stakeholders using data presented via Power BI, Tableau, and Matplotlib.
• Programming: By mastering languages like Python, R, and SQL, you will be able to write efficient code for automating data processes and data models.
• Exposure to Hadoop, Spark, and Cloud computing platforms while managing and analyzing big data.
• Critical thinking and problem solving: With the data science course you will be well equipped to approach business problems with the right analytical tools and can come up with relevant data-driven solutions for the firm.
• Domain Knowledge: You will gain specialized industry-specific knowledge. This will help you in applying these data science principles to healthcare, finance, marketing, etc.
• Communication Skills: Lastly, the course will enable you to communicate technical data analysis in the most actionable business insights for non-technical stakeholders.

Career Scope of Data Science

Data science has emerged as one of the most in-demand fields with job opportunities expanding rapidly across various sectors. As companies increasingly rely on data for decision-making and innovation, the scope of careers in data science continues to grow. Here are key aspects of the career scope in data science:  

• Demand Across Various Sectors: Data scientists are in high demand across various industries such as finance, healthcare, retail, technology, and manufacturing. Leading tech companies like Google, Amazon, and Microsoft recruit data scientists. Meanwhile, traditional industries are rapidly building data science teams to drive digital transformation. Given the broad applicability of data science skills, professionals in this field are highly valuable across nearly every sector.
• Attractive Salaries: Professionals in this field earn competitive salaries that grow with experience and expertise. The substantial gap between the demand and supply of data science professionals contributes to higher pay.
• Wide Range of Career Roles: Data science offers a range of career opportunities and based on your skills and interests you may apply for job roles such as:
  • Data Scientist
  • Machine Learning Engineer
  • Data Engineer
  • AI Researcher
  • Business Intelligence Analyst
• Opportunities for Career Growth: With continuous advancements in AI, deep learning, and big data analytics, many opportunities exist for professionals to learn and enhance their profiles. These growth opportunities include senior or specialized roles, such as:
  • Chief Data Officer
  • Chief Analytics Officer
• Job Stability: The growing reliance on data-driven decision-making provides strong job security in the data science field. As organizations continue to digitize their operations and expand their data capabilities, the demand for data science professionals remains consistently high. Even during economic downturns, the field has proven resilient, as companies prioritize data-driven efficiency and innovation.
• Global Career Opportunities: Data science skills are highly valued across the globe, providing opportunities to work internationally. Many roles offer remote work options, allowing professionals to collaborate with companies worldwide while enjoying a good work-life balance. The international scope of the field also fosters cultural exchange and diverse work experiences.

Data Science Salary

Data Science is a high-paying and in-demand career. Salaries are influenced by experience level, industry type, skill set, and location. Professionals with expertise in advanced analytics, machine learning, and cloud technologies tend to command higher salaries. Industries like finance, IT, and e-commerce offer some of the most competitive pay scales. Additionally, salaries vary based on geographic location, with metro cities offering higher compensation than smaller towns. 

Here is a list of data scientist salaries in India across different cities:

City	Entry-Level (0-2 years)	Mid-Level (2-5 years)	Senior-Level (5+ years)
Bengaluru	₹8 LPA  – ₹12 LPA	₹15 LPA – ₹25 LPA	₹30 LPA – ₹50 LPA
Mumbai	₹7 LPA – ₹11 LPA	₹13 LPA – ₹22 LPA	₹28 LPA – ₹45 LPA
Delhi NCR	₹6 LPA – ₹ 10 LPA	₹12 LPA – ₹20 LPA	₹25 LPA – ₹40 LPA
Hyderabad	₹6 LPA – ₹ 10 LPA	₹12 LPA – ₹20 LPA	₹25 LPA – ₹40 LPA
Chennai	₹5 LPA – ₹ 9 LPA	₹10 LPA – ₹18 LPA	₹20 LPA – ₹35 LPA
Pune	₹5 LPA – ₹ 9 LPA	₹10 LPA – ₹18 LPA	₹20 LPA – ₹35 LPA
Kolkata	₹4 LPA – ₹8 LPA	₹8 LPA – ₹15 LPA	₹15 LPA – ₹30 LPA

Top Companies Hiring Data Scientists on Internshala

Leading companies recruit data scientists via Internshala, offering exciting opportunities to work on real-world data-driven solutions. These organizations span various industries, from AI and analytics to finance and technology, providing strong career growth in the right direction. Here are some of the top companies currently hiring for data scientist positions: 

• Persist Ventures
• DataVinci Private Limited
• Swissmote, Inc.
• Q Blocks
• Indika AI Private Limited
• Systemic Altruism
• Renan Partners
• DeepThought Edutech Ventures Private Limited
• Robust Tech Solutions
• WORDING

Key Features of Internshala’s Data Science Course with Placement Assistance

Internshala’s data science course with placement guarantee equips students with the right skills and helps them establish themselves in this field. In addition, the course provides job placement assistance. The key features of the course include:

• Comprehensive Curriculum: The curriculum is well-balanced and contains foundational and advanced topics in data science, including Python, machine learning, statistics, data visualization, and deep learning. Students gain hands-on experience with TensorFlow, Pandas, and Scikit-learn.

• Industry-Driven Projects: Internshala provides industry-relevant projects and case studies that allow students to apply their learning to real-world situations. Such projects help build a robust portfolio that enables students to showcase their practical skills to potential employers.

• Placement Assistance: Placement assistance is one such advantage of this course. Upon the successful completion of the course, Internshala assures placement assistance for the students in placement roles with top companies in the data science world.

• Expert Mentorship and Support: Students receive personalized guidance and mentorship from industry experts, helping them navigate complex concepts and projects. Through one-on-one mentoring, students can gain an understanding of the course and its applications.

• Flexible Learning: The learning is flexible and students can study at their own pace. The course comprises recorded sessions, live interactive classes, and resources that can be accessed later. Hence, learning becomes exclusively convenient and accessible.

• Mock Interviews and Resume Building: The course also offers mock interviews and professional resume-building sessions to ensure the students are well-prepared for their career placements. These activities help build self-confidence and fine-tune skills that may be required to secure a job in the competitive data science field.

• Job Assistance and Career Services: In addition to placement assistance, Internshala provides a career services portfolio. This includes mock interviews, job alerts, and career counseling. These services help students hone their skills and navigate the hiring process effectively to land their ideal roles.

Conclusion

Upon completing this data science syllabus you will have a strong understanding of key concepts, tools, and techniques that you need at the end in order to analyze data effectively when completing any data science course. The courses come with practical skills in data manipulation, statistical analysis, machine learning, and data visualization that empower you to draw meaningful insights from complex datasets.

Most importantly, at the end of the course, you will have a strong portfolio of projects that show your ability to deal with data-driven challenges. If you want to know more about career opportunities in this field then you can also check out our blog on data science career opportunities 

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