Data Science Course Overview

Master the Essentials of Data Science: Become a Certified Data Expert and Drive Insights from Big Data!

Rohil NextGen offers a comprehensive Data Science training program covering Data Modeling, Data Visualization, Statistical Analysis, and Machine Learning Techniques using industry-standard tools.

TOP RATED 4.9 4.9 Ratings
6 Months
Course Duration
2025
Latest Curriculum
150
Total Hours
80
Theory Hours

Why Choose Data Science Certification?

Annual Income

Estimated Salary

₹ 18 L

Career Opportunities

Growth Rate This Year

50%

Industry Demand

Projected by 2026

5 L

Become a Data Science Expert

❝ Learn Data Modeling, Visualization, and Machine Learning ❞

Course Overview

This Data Science course is designed to teach you how to analyze and interpret large datasets using tools like Python, R, SQL, and Tableau. You will learn about statistical analysis, data modeling, predictive analytics, and how to visualize data to derive actionable insights.

Key Learning Outcomes

  • Understand the fundamentals of data cleaning and preparation.
  • Master data visualization techniques using Tableau, Power BI, and other tools.
  • Learn statistical methods for data analysis and interpretation.
  • Gain hands-on experience with machine learning algorithms and techniques.
  • Build predictive models and data-driven applications using data science tools.

Career Prospects with This Course

  • Become a Data Analyst, Data Scientist, or Business Intelligence Specialist in leading tech companies.
  • Work as a Data Engineer, optimizing data pipelines and managing data infrastructures.
  • Pursue roles in machine learning engineering and AI-driven analytics.
  • Engage in academic research and development in data science and analytics.
  • Lead data-driven decision-making in business and organizations with advanced analytical techniques.

Data Science – 180 Days (Theory + Practical)

Tools: Python, NumPy, Pandas, Matplotlib, Scikit-learn, SQL, Jupyter, Git, Tableau (optional Power BI)

Phase 1: Python for Data Science (Weeks 1–4)

Week 1-2: Python Fundamentals

  • Data types, variables, operators
  • Conditional logic, loops
  • Functions, modules, error handling

Practical

  • Calculator, basic file I/O
  • Mini grading system using functions

Week 3-4: Python Data Structures & Libraries

  • Lists, Tuples, Sets, Dictionaries
  • Intro to NumPy arrays
  • Pandas: Series, DataFrames

Practical

  • Weather dataset analysis (Pandas)
  • NumPy matrix operations

Phase 2: Data Analysis & Visualization (Weeks 5–8)

Theory

  • Data cleaning and preprocessing techniques
  • Exploratory Data Analysis (EDA)
  • Data visualization with Matplotlib and Seaborn
  • Interactive visualization with Plotly

Practical

  • Analyze and visualize real-world datasets
  • Create interactive dashboards
  • Data cleaning and transformation projects

Phase 3: Statistics & Probability (Weeks 9–12)

Theory

  • Descriptive statistics (mean, median, mode, variance)
  • Probability distributions
  • Hypothesis testing and confidence intervals
  • Correlation and regression analysis

Practical

  • Statistical analysis of business data
  • A/B testing implementation
  • Regression modeling projects

Phase 4: SQL & Databases (Weeks 13–15)

Theory

  • Database fundamentals and SQL syntax
  • Complex queries, joins, and subqueries
  • Database design and normalization
  • Working with NoSQL databases

Practical

  • Database design and implementation
  • Complex SQL query writing
  • Data extraction and transformation

Phase 5: Machine Learning Foundations (Weeks 16–22)

Theory

  • Supervised vs Unsupervised Learning
  • Linear and Logistic Regression
  • Decision Trees and Random Forests
  • Clustering algorithms (K-means, Hierarchical)
  • Model evaluation and validation techniques

Practical

  • Build predictive models for real datasets
  • Implement classification and regression algorithms
  • Create clustering solutions for customer segmentation

Phase 6: Advanced ML & Deployment (Weeks 23–26)

Theory

  • Neural Networks and Deep Learning
  • Natural Language Processing (NLP)
  • Time Series Analysis and Forecasting
  • Model deployment and MLOps

Practical

  • Build and train neural networks
  • Text classification and sentiment analysis
  • Deploy machine learning models

Frequently Asked Questions

What is Data Science?

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines statistics, mathematics, programming, and domain expertise to solve complex problems.

Do I need programming experience for this Data Science course?

Basic programming knowledge is helpful but not mandatory. The course starts with Python fundamentals and gradually builds up to advanced data science concepts. We provide comprehensive support for beginners to become proficient in programming for data analysis.

Will I receive a certificate after completing the Data Science course?

Yes, upon successful completion of the course and final project, you will receive a certificate from Rohil NextGen that validates your Data Science skills and can be shared with employers.

What kind of projects will I work on during the course?

You'll work on real-world data science projects including data analysis, predictive modeling, machine learning implementations, data visualization dashboards, and a comprehensive capstone project that integrates all the skills you've learned.

What tools and technologies will I learn?

You'll learn Python for data analysis, SQL for database management, statistical analysis techniques, machine learning algorithms, data visualization tools (Tableau, Matplotlib, Seaborn), and various data science libraries (NumPy, Pandas, Scikit-learn).

What career support do you provide after course completion?

We provide comprehensive career support including resume building, interview preparation, portfolio development guidance, and connections with our placement partners for job opportunities in data analysis, data science, and related fields.