Complete Data Science & Analytics Course in Ahmedabad

Data Science Comprehensive Course Outline

Module 1: Introduction to Python Programming

1.1 Basics of Python

• Introduction to Python

• Installing Python and IDEs (Jupyter, PyCharm, VSCode)

• Writing your first Python program

1.2 Variables and Data Types

• Variables and Constants

• Data types: Integer, Float, String, Boolean, and Complex

• Type conversion and casting

1.3 Control Structures

• Conditional Statements (if, elif, else)

• Loops: for, while

• Break, Continue, and Pass

1.4 Functions and Modules

• Defining and calling functions

• Function arguments and return values

• Importing and using Python libraries

1.5 Advanced Python Concepts

• File handling: Reading and writing files

• Error and Exception handling

• Object-Oriented Programming: Classes, Objects, Inheritance, Polymorphism

Module 2: Statistics and Probability

2.1 Basics of Statistics

• Descriptive statistics: Mean, Median, Mode, Variance, Standard Deviation

• Data visualization with graphs (Histogram, Boxplot)

2.2 Probability Concepts

• Basic probability rules

• Conditional probability and Bayes' theorem

2.3 Statistical Distributions

• Normal, Binomial, and Poisson distributions

• Central Limit Theorem

2.4 Hypothesis Testing

• Null and Alternative Hypotheses

• p-values and significance levels

• T-tests and Chi-Square tests

2.5 Data Inferences

• Confidence intervals

• Correlation vs. Causation

Module 3: Data Manipulation and Visualization

3.1 Data Manipulation with Pandas

• DataFrames and Series

• Data cleaning: Handling missing values, duplicates

• Filtering, sorting, and grouping data

3.2 Numerical Computations with NumPy

• Arrays: Creation and operations

• Indexing, slicing, and broadcasting

• Statistical and mathematical operations

3.3 Data Visualization with Matplotlib

• Line, Bar, and Pie charts

• Customizing plots: Titles, labels, legends

• Subplots

3.4 Advanced Visualization with Seaborn

• Pairplots, Heatmaps, and Violin plots

• Customizing Seaborn styles

• Multi-variable visualizations

Module 4: Machine Learning

4.1 Introduction to Machine Learning

• Supervised vs. Unsupervised learning

• Overview of machine learning workflow

4.2 Supervised Learning Techniques

• Regression: Linear and Logistic Regression

• Decision Trees and Random Forests

• Support Vector Machines (SVMs)

4.3 Unsupervised Learning Techniques

• Clustering: K-Means, Hierarchical Clustering

• Dimensionality Reduction: PCA

4.4 Practical Implementation

• Using Scikit-Learn for ML models

• Model evaluation: Accuracy, Precision, Recall, F1 Score

• Cross-validation and hyperparameter tuning

Module 5: Deep Learning and Artificial Intelligence

5.1 Fundamentals of Deep Learning

• Introduction to neural networks

• Activation functions and loss functions

5.2 TensorFlow Basics

• Setting up TensorFlow

• Building and training neural networks

5.3 Advanced Deep Learning

• Convolutional Neural Networks (CNNs) for computer vision

• Recurrent Neural Networks (RNNs) for sequence modeling

• Transfer learning and pre-trained models

5.4 Natural Language Processing (NLP)

• Text preprocessing

• Sentiment analysis using LSTMs

• Word embeddings (Word2Vec, GloVe)

Module 6: Data Tools and Dashboards

6.1 Data Visualization Tools

• Overview of Tableau and Power BI

• Connecting to datasets

6.2 Dashboard Creation

• Designing dynamic dashboards

• Adding filters and interactivity

• Real-time data updates

6.3 Case Studies

• Sales analysis dashboards

• Marketing campaign performance visualization

Module 7: Projects and Applications

7.1 Real-World Projects

• Dashboard Creation: Build an interactive sales performance dashboard using Power BI

• Customer Churn Prediction: Analyze and predict customer churn using machine learning

• Sentiment Analysis: Develop a sentiment analysis model for product reviews

• Facial Recognition System: Build a facial recognition system using TensorFlow

7.2 Capstone Project

• Choose a comprehensive project combining multiple modules (e.g., predictive analytics and dashboard design).

Why Enroll?

By completing this course:

• You’ll master essential data science tools like Python, Scikit-Learn, TensorFlow, and Tableau.

• You’ll gain hands-on experience with real-world datasets and projects.

• You’ll build a professional portfolio to showcase your skills to employers.