Master Data Science – Full Online Course for Beginners & Professionals

Foundations of Data Science

Gain a clear understanding of what data science truly is—not just from a theoretical standpoint but in terms of how it’s applied across industries. This module introduces the data science workflow, core responsibilities, and the mindset required to transition from data consumer to data-driven decision maker.

Statistics for Data-Driven Thinking

Understand how to interpret data before trying to predict it. You’ll learn how to apply statistical techniques such as mean, median, variance, skewness, probability distribution, and hypothesis testing, including T-test, Chi-Square, and ANOVA—essentials for building robust machine learning models.

Applied Mathematics & Probability for Machine Learning

Master the essential math that powers modern ML algorithms. You’ll gain hands-on experience with vectors, matrices, eigenvalues, and probability theory including Poisson, Bernoulli, and Normal distributions, all tied back to practical ML use cases.

Data Acquisition with Web Scraping

Real-world data is messy—and rarely in Excel. Learn how to use BeautifulSoup to scrape structured and unstructured data from websites. Build your own datasets instead of relying on pre-cleaned ones, enhancing both your project quality and confidence in real data environments.

Numerical Computing with NumPy

Get comfortable with 1D, 2D, and ND arrays using NumPy. Learn how to perform vectorized operations, statistical analysis, and foundational data manipulation. This is your first step in building optimized, high-performance data pipelines

Data Manipulation with Pandas

Learn how to clean, transform, and organize raw data using Pandas. From handling null values to filtering, merging, and grouping large datasets, this module equips you with the tools to drive data exploration and model readiness.

Data Visualization with Matplotlib & Seaborn

Visualization is how you make data understandable to stakeholders. Learn to build charts that explain complex ideas clearly—bar plots, boxplots, heatmaps, scatterplots, and more. Learn how to use visual cues to tell data stories with business impact

Feature Engineering & Data Preprocessing

Your model is only as good as your data. Learn advanced techniques to clean and transform features:

• Imputation techniques (mean, median, mode)

• Encoding strategies (label, one-hot, ordinal)

• Handling outliers, scaling, normalization
  These skills are essential for building production-ready models in real-world environments

Machine Learning Algorithms with Scikit-Learn

Build and deploy actual machine learning models:

• Linear & Logistic Regression

• K-Nearest Neighbors (KNN)

• K-Means Clustering

• Decision Trees & Random Forests
  You’ll not only learn how these algorithms work but also when and why to use them, with performance evaluation using confusion matrix, precision, recall, and F1-score.

Natural Language Processing with NLTK

Move beyond numbers and learn how machines understand human language. Explore text cleaning, tokenization, stemming, lemmatization, bag-of-words, and build a basic text classifier using Naive Bayes. This opens doors to careers in sentiment analysis, chatbots, and AI applications.

Real-World Projects for Portfolio Building

Apply everything through capstone projects:

1. IPL & COVID EDA – Build dashboards and trends using public datasets

2. Bike Sharing Demand Forecast – Create a time-series prediction model for ride demand

3. Comcast Telecom Complaints – Clean, visualize, and classify real-world text data

These projects simulate actual business problems, preparing you for job interviews and client engagements.

Career Acceleration & Certification

We don’t just train—you get placement assistance, mock interviews, resume building, and project portfolio support.
You’ll also receive an industry-recognized certificate, plus optional mentoring to help you crack freelancing, internships, or full-time data science roles.