EV GYAN(15): Data Science using Python

Course Overview:

The course "Data Science Using Python" is designed to provide comprehensive training in data science principles and Python programming. It covers data analysis, machine learning, data visualization, and big data handling using Python. The course is tailored to equip students with practical skills to analyze, interpret, and visualize data, making data-driven decisions applicable across various industries, including the growing field of electric vehicles (EVs).

Course Objectives:

Introduce fundamental concepts of data science.

Develop proficiency in Python programming for data analysis and visualization.

Teach methods for collecting, processing, and analyzing data.

Apply machine learning techniques to solve real-world problems.

Provide hands-on experience with real-world datasets.

Key Topics:

Introduction to Data Science:

Overview of Data Science: Understanding the role and importance of data science in today's data-driven world.

Applications of Data Science: Examples from various industries, including finance, healthcare, marketing, and automotive.

Data Science Workflow: Steps involved in a typical data science project: data collection, data preprocessing, data analysis, model building, and deployment.

Python Programming Basics:

Python Environment Setup: Installing Python and relevant libraries, using Jupyter notebooks.

Basic Python Syntax: Variables, data types, operators, and control structures (if statements, loops).

Functions and Modules: Creating and using functions, importing modules, and understanding libraries.

Data Manipulation with Pandas:

Pandas Overview: Introduction to Pandas library for data manipulation.

DataFrames: Creating, indexing, and modifying DataFrames.

Data Cleaning: Handling missing values, duplicates, and outliers.

Data Transformation: Applying functions, grouping, merging, and reshaping data.

Data Visualization:

Matplotlib: Basic plotting with Matplotlib, creating line plots, scatter plots, bar charts, histograms, and customizing plots.

Seaborn: Advanced visualization with Seaborn, creating attractive and informative statistical graphics.

Plotly: Interactive plots with Plotly, creating interactive dashboards.

Exploratory Data Analysis (EDA):

Descriptive Statistics: Summarizing data using mean, median, mode, variance, and standard deviation.

Data Distributions: Visualizing distributions with histograms, KDE plots, and box plots.

Correlation and Causation: Analyzing relationships between variables using correlation coefficients and scatter plots.

Machine Learning with Scikit-Learn:

Introduction to Machine Learning: Basics of machine learning, types of learning (supervised, unsupervised, reinforcement).

Supervised Learning: Regression (linear regression, decision trees, random forests) and classification (logistic regression, support vector machines, KNN).

Unsupervised Learning: Clustering (K-means, hierarchical clustering) and dimensionality reduction (PCA).

Model Evaluation: Metrics for regression and classification (MAE, MSE, accuracy, precision, recall, F1-score).

Advanced Machine Learning:

Hyperparameter Tuning: Grid search, random search, and cross-validation techniques.

Ensemble Methods: Boosting (AdaBoost, Gradient Boosting) and bagging (Random Forest, Bagging classifiers).

Model Deployment: Saving and loading models, deploying models with Flask and Docker.

Time Series Analysis:

Time Series Data: Characteristics of time series data, handling time series in Pandas.

Time Series Decomposition: Trend, seasonality, and residual analysis.

Forecasting Models: ARIMA, SARIMA, and exponential smoothing.

Natural Language Processing (NLP):

Text Processing: Tokenization, stemming, lemmatization, and stopword removal.

Vectorization: Bag of Words, TF-IDF, and word embeddings.

Text Classification: Sentiment analysis, topic modeling, and named entity recognition.

Big Data Handling:

Introduction to Big Data: Understanding big data concepts and challenges.

Hadoop and Spark: Basics of Hadoop ecosystem and Apache Spark for big data processing.

PySpark: Using PySpark for big data analysis and machine learning.

Deep Learning with TensorFlow and Keras:

Introduction to Neural Networks: Basics of neural networks, activation functions, and architectures.

Building Neural Networks: Using TensorFlow and Keras to build and train neural networks.

Deep Learning Applications: Image recognition, natural language processing, and generative models.

Capstone Projects:

Real-World Data Science Projects: Applying the knowledge gained to real-world datasets.

End-to-End Project: Conducting an entire data science project from data collection to model deployment.

Presentation and Reporting: Presenting findings, writing technical reports, and visualizing results effectively.

Practical Labs and Projects:

Hands-On Exercises: Weekly labs focusing on applying concepts to real datasets.

Mini-Projects: Smaller projects throughout the course to reinforce learning.

Capstone Project: A comprehensive project that involves solving a real-world problem using data science techniques.

Assessment:

Quizzes and Exams: Periodic assessments to test theoretical understanding.

Assignments and Lab Reports: Regular assignments and lab exercises to evaluate practical skills.

Project Reports and Presentations: Evaluating the ability to conduct and present data science projects.

Career Opportunities:

Data Scientist: Analyzing and interpreting complex data to help companies make better decisions.

Data Analyst: Working with data to identify trends and patterns.

Machine Learning Engineer: Building and deploying machine learning models.

Business Analyst: Using data to inform business strategies and operations.

Big Data Engineer: Handling and analyzing large volumes of data using big data technologies.