Data Science Course
Data Science is a game-changer in today’s world, enabling businesses and organizations to
make data-driven decisions and uncover hidden patterns. It combines statistical methods,
programming, and domain expertise to derive actionable insights. From predicting
customer behavior to optimizing supply chains, Data Science is revolutionizing industries
like healthcare, finance, e-commerce, and technology. Its application extends to artificial
intelligence, making machines smarter and more efficient.
Learn how to source, manipulate and visualise data using Python and its libraries. Build and refine your Machine Learning skills with the help of topics like Statistics, Trees, Neural Networks etc.
Projects the you will build
Case Study on Indian Startups: Detailed analysis of the Indian Startups for interpretation of trends and patterns to facilitate selection of proper city, useful investors, funding type etc for different startups.
Text Classification: Build a classifier model using Naive Bayes algorithm to predict the topic of an article present in a newspaper.
Image Classification: Build a classifier for classifying 10,000 images into 10 classes (dog, horse, cat etc) using the CIFAR-10 Dataset.
Twitter Sentiment Analysis: Analyse the tweets posted on twitter to predict the sentiment of the tweet i.e. positive, negative or neutral
Data Science full Syllabus and Course OverView
1. Introduction to Data Science
• What is Data Science?
• Applications of Data Science in Real Life
• Data Science Workflow
• Key Roles in Data Science: Data Scientist, Analyst, Engineer
2. Essential Mathematics for Data Science
• Linear Algebra Basics
• Probability and Statistics Essentials
• Calculus for Optimization
• Understanding Distributions and Hypothesis Testing
3. Data Collection and Integration
• Structured vs. Unstructured Data
• Data Integration Techniques
• Data Sources: APIs, Databases, Web Scraping
• Handling Big Data: Hadoop and Spark
4. Data Preprocessing
• Data Cleaning: Handling Missing and Outlier Data
• Data Transformation and Encoding
• Feature Scaling and Normalization
• Splitting Data: Train-Test-Validation Sets
5. Exploratory Data Analysis (EDA)
• Descriptive Statistics
• Data Visualization with Python: Matplotlib, Seaborn
• Identifying Relationships and Correlations
• Techniques for Detecting Patterns
6. Machine Learning Basics
• Supervised vs. Unsupervised Learning
• Algorithms Overview: Regression, Classification, Clustering
• Overfitting and Underfitting
• Evaluating Model Performance: Accuracy, Precision, Recall
7. Advanced Machine Learning
• Neural Networks and Deep Learning Fundamentals
• Ensemble Methods: Random Forest, Gradient Boosting
• Natural Language Processing Basics
• Recommendation Systems
8. Data Visualization and Communication
• Importance of Storytelling with Data
• Tools: Tableau, Power BI, Python Visualization Libraries
• Effective Chart Design Principles
• Creating Dashboards
9. Big Data and Cloud Technologies
• Introduction to Big Data Frameworks: Hadoop, Spark
• Cloud Platforms for Data Science: AWS, Azure, Google Cloud
• Implementing Scalable Data Pipelines
10. Real-World Projects and Case Studies
• Sentiment Analysis of Social Media Data
• Sales Forecasting for Retail Businesses
• Customer Segmentation Using Clustering
• Fraud Detection in Financial Transactions
• Predictive Modeling for Healthcare
Lets Work Together
No matter whatever career path you choose in the field of computer science, Data Science keeps you ahead of all.
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