Learn Data Science and Build Your Career in Tech

BASIC

• What is data science, and why does it play a crucial role in today’s data-driven world?
• Describe the various paths that can lead to a career in data science.
• Summarize tips shared by experienced data scientists for beginners in the field.
• Describe why data science is regarded as the top job of the 21st century.

Tools for Data Science

• Explain the essential tools in a Data Scientist’s toolkit, which include Libraries and Packages, Datasets, Machine Learning Models, and Big Data Tools.
• Use programming languages frequently used by data scientists, such as Python, R, and SQL.
• Show proficiency in using tools like Jupyter Notebooks and RStudio, and leverage their different features effectively.
• Develop and handle source code for data science projects using Git repositories and GitHub.

• Explain what a data science methodology is and why it is essential for data scientists to use one.
• Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study.
• Assess which analytic model—predictive, descriptive, or classification—is most suitable for analyzing a case study.
• Choose the appropriate data sources for your data science analysis methodology.

• Learn Python, the leading programming language for Data Science and Software Development.
• Use Python programming concepts such as variables, data structures, branching, loops, functions, objects, and classes to develop logical solutions.
• Show expertise in utilizing Python libraries like Pandas and Numpy, and in writing code with Jupyter Notebooks.
• Access and extract data by web scraping using APIs and Python libraries such as Beautiful Soup.

• Take on the role of a Data Scientist or Data Analyst working on an actual project.
• Showcase your expertise in Python – the preferred language for Data Science and Data Analysis.
• Utilize Python fundamentals and data structures to effectively work with data in Python.
• Create a dashboard in Python using libraries such as Pandas, Beautiful Soup, and Plotly within a Jupyter Notebook environment.

• Examine data stored in a database by using SQL and Python for analysis.
• Design a relational database and manage multiple tables using Data Definition Language (DDL) commands.
• Create SQL queries ranging from basic to intermediate level using DML (Data Manipulation Language) commands.
• Create more efficient and impactful queries using advanced SQL techniques such as views, transactions, stored procedures, and joins.

• Write Python code to clean and prepare data for analysis. This includes handling missing values, formatting the data, normalizing values, and binning the data into appropriate categories.
• Conduct exploratory data analysis and apply analytical methods to real-world datasets using tools like Pandas, Numpy, and Scipy libraries.
• Manipulate data with data frames, summarize key information, analyze data distribution, perform correlation analysis, and build data pipelines.
• Develop and assess regression models using the machine learning scikit-learn library, and apply them for making predictions and informed decisions.

• Explain the different types of machine learning algorithms and the situations in which each should be used.
• Compare and contrast linear classification methods, including multiclass prediction, support vector machines (SVM), and logistic regression.
• Write Python code to implement different classification techniques, such as K-Nearest Neighbors (KNN), Decision Trees, and Regression Trees.
• Assess the outcomes of simple linear, non-linear, and multiple regression models on a dataset by applying various evaluation metrics. 

• Show expertise in data science and machine learning methods by analyzing a real-world data set and preparing a comprehensive report for stakeholders.
• Utilize your skills to gather data, clean and preprocess it, perform exploratory data analysis, create visualizations, develop models, and evaluate model performance.
• Write Python code to build machine learning models, including Support Vector Machines (SVM), Decision Tree Classifiers, and K-nearest neighbors (KNN).
• Assess the performance of machine learning models used for predictive analysis, comparing their strengths and weaknesses. Identify the model that delivers the best results based on accuracy, efficiency, and suitability for the task at hand.

• Leverage generative AI tools, like GPT 3.5, ChatCSV, and tomat.ai, available to Data Scientists for querying and preparing data
• Explore real-world examples where generative AI can improve data science workflows.
• Practice generative AI skills in hands-on labs and projects by generating and augmenting datasets for specific use cases
• Use generative AI techniques to enhance the development and improvement of machine learning models.

• Explain the role of a data scientist, the different career paths available, and the potential opportunities within the field.
• Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.
• Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.
• Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.

Hands-On Learning: Work on real-world projects to gain practical knowledge.

Beginner-Friendly: Start with the basics and move to advanced topics step by step.

Flexible Schedule: Study at your own pace with online or offline options.

Updated Curriculum: Learn the latest tools like Python, Tableau, and TensorFlow.

Career Support: Get guidance on resumes, interviews, and job placements.

Globally Recognized Certificate: Boost your career with a certification.

Tools You Will Learn

Gain hands-on experience with the key programming languages and tools commonly used in data science courses.

1. Advanced Excel: Learn advanced features and tools for analyzing data, building models, and creating reports.
2. SQL: Master the skills to manage and query large datasets effectively, which serves as the core of database management.
3. Python: Acquire practical experience using Python for tasks such as data cleaning, analysis, and building machine learning models.
4. Tableau: Design powerful visualizations and dashboards to analyze and present complex datasets in an easy-to-understand way.
5. Power BI: Create interactive reports and integrate data to gain comprehensive business insights.
6. Hadoop: Learn how to store and process large volumes of data using distributed systems.

Optional modules offer the chance to deepen your expertise in specific areas of interest.

Need to know more?

Download the course brochure to explore the full details of our Data Science course.

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Career Opportunities After a Postgraduate Degree in Data Science

A postgraduate degree in data science equips you with advanced skills and expertise, opening doors to high-paying and impactful careers in India and abroad.

Top Career Roles in Data Science

Data Scientist

Analyze data using AI, machine learning, and advanced analytics to deliver actionable insights.

Average Salary: ₹8–19 LPA

Machine Learning Engineer

Develop machine learning models to solve real-world problems.

Average Salary: ₹6–18 LPA

AI Specialist / Research Scientist

Build AI systems and innovate with deep learning technologies.

Average Salary: ₹7–23.3 LPA

Big Data Engineer

Manage massive datasets and design scalable data systems.

Average Salary: ₹5–14.1 LPA

Data Science Consultant

Provide businesses with strategic insights based on data.

Average Salary: ₹15–23 LPA

Business Intelligence (BI) Manager

Lead data-driven decision-making through advanced tools.

Average Salary: ₹14–27.5 LPA

Quantitative Analyst

Use data science in financial modeling for investments.

Average Salary: ₹12–25 LPA

India vs Abroad: Career Outcomes in Data Science

Data Science opens global opportunities with excellent salary prospects. The specific role and earnings depend on location, sector, and expertise.

Technology: Leading companies like Adobe, Oracle, SAP, and Salesforce actively seek data scientists to build innovative products and solutions.

Finance: Banks such as HDFC, ICICI, JPMorgan, and Goldman Sachs, as well as fintech companies like PayPal and Square, rely on data science to enhance decision-making and improve services.

Healthcare: Organizations like Apollo Hospitals, Siemens Healthineers, Medtronic, and Cerner Corporation use data science to personalize treatment plans, predict diseases, and manage patient care more effectively.

Retail & E-commerce: Companies like Flipkart, Nykaa, Meesho, Shopify, Target, and Costco leverage data to optimize inventory, improve customer experiences, and boost sales.

Manufacturing: Data scientists help Tata Steel, Siemens, General Electric, and Caterpillar streamline operations, improve product quality, and reduce costs using predictive analytics.

Logistics & Supply Chain: Companies such as Blue Dart, FedEx, DHL, Maersk, and Delhivery rely on data science for route optimization, demand forecasting, and inventory management.

Telecommunications: Telecom giants like Bharti Airtel, Reliance Jio, Verizon, Vodafone, and AT&T use data to enhance network performance, optimize customer retention, and prevent fraud.