Data Science Course in Nagpur with Python, AI & Machine Learning

• Basic computer concepts
• File handling basics
• Software installation and setup
• VS Code, Jupyter Notebook, Google Colab and Anaconda setup
• Basic command line usage
• Git and GitHub basics
• Creating a professional GitHub profile
• Uploading projects on GitHub

• Introduction to Python
• Variables and data types
• Operators and expressions
• Conditional statements
• Loops
• Functions and lambda functions
• List, Tuple, Set and Dictionary
• String handling
• Date and time handling
• File handling
• Exception handling
• Modules and packages
• Object-Oriented Programming basics
• Practical programs: calculator, student marks analysis, salary calculation, login system and file automation

• Number system basics
• Algebra basics
• Functions and linear equations
• Logarithms
• Matrices and vectors
• Dot product and matrix multiplication
• Eigenvalues and eigenvectors basics
• Calculus introduction
• Derivatives
• Gradient concept
• Optimization basics
• Cost function concept

• Population and sample
• Numerical, categorical, ordinal and nominal data
• Mean, median and mode
• Range, variance and standard deviation
• Percentile and quartile
• Skewness and kurtosis
• Correlation and covariance
• Probability basics
• Conditional probability
• Bayes theorem
• Normal, binomial and Poisson distribution
• Sampling techniques
• Central Limit Theorem
• Hypothesis testing
• p-value, t-test, z-test, chi-square test and ANOVA basics

• Excel interface and formatting
• Sorting and filtering
• Conditional formatting
• Data validation
• Text, date and logical functions
• VLOOKUP, HLOOKUP, XLOOKUP and INDEX MATCH
• Pivot tables and pivot charts
• Dashboard creation
• Data cleaning in Excel
• Power Query basics
• Business report preparation
• Projects: sales dashboard, attendance dashboard, student performance dashboard and expense dashboard

• Introduction to databases
• DBMS vs RDBMS
• MySQL installation
• Database and table creation
• Data types
• Primary key and foreign key
• SELECT, WHERE, ORDER BY, GROUP BY, HAVING and LIMIT
• Aggregate functions: COUNT, SUM, AVG, MIN and MAX
• INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN, SELF JOIN and CROSS JOIN
• Subqueries
• Views
• Stored procedures basics
• Window functions
• CTE
• Business query solving
• Projects: customer analysis, e-commerce order analysis, employee salary analysis and sales reports

NumPy

• Arrays, indexing, mathematical operations, statistical operations and matrix operations

Pandas

• Series and DataFrame
• Reading CSV, Excel and JSON files
• Data selection and filtering
• Handling missing and duplicate values
• GroupBy, merge, join, pivot tables and data transformation

Matplotlib & Seaborn

• Line chart, bar chart, pie chart, histogram and scatter plot
• Count plot, box plot, heatmap, pair plot and correlation visualization

• What is dirty data?
• Missing value treatment
• Duplicate value removal
• Outlier detection and treatment
• Data type conversion
• Feature scaling
• Standardization and normalization
• Label encoding and one-hot encoding
• Data transformation
• Feature engineering
• Train-test split
• Handling imbalanced data
• Practical work: clean sales, HR and customer datasets

• What is EDA?
• Understanding dataset structure
• Data summary
• Univariate analysis
• Bivariate analysis
• Multivariate analysis
• Correlation analysis
• Outlier analysis
• Pattern detection
• Business insight generation
• Data storytelling
• Projects: Titanic analysis, IPL analysis, e-commerce sales analysis, customer behavior analysis and employee attrition analysis

Power BI

• Power BI Desktop installation
• Importing data
• Power Query Editor
• Data cleaning in Power BI
• Data modeling and relationships
• DAX basics
• Measures and calculated columns
• Cards, charts, slicers and filters
• Dashboard design and publishing reports

Tableau

• Connecting data sources
• Worksheets, dashboards and stories
• Filters, parameters and calculated fields
• Interactive dashboard creation

• What is Machine Learning?
• AI vs ML vs Deep Learning
• Supervised learning
• Unsupervised learning
• Reinforcement learning introduction
• Features and labels
• Training and testing data
• Model training and prediction
• Model evaluation
• Overfitting and underfitting
• Bias and variance
• Cross-validation
• Model selection

Regression Algorithms

• Simple linear regression
• Multiple linear regression
• Polynomial regression
• Ridge regression
• Lasso regression
• Decision tree regression
• Random forest regression

Classification Algorithms

• Logistic regression
• K-nearest neighbors
• Decision tree classifier
• Random forest classifier
• Support Vector Machine
• Naive Bayes
• Gradient boosting basics
• XGBoost introduction

Evaluation Metrics

• Accuracy, precision, recall and F1-score
• Confusion matrix
• ROC curve and AUC score
• MAE, MSE, RMSE and R² score

• Clustering introduction
• K-Means clustering
• Hierarchical clustering
• DBSCAN basics
• Principal Component Analysis
• Dimensionality reduction
• Association rule mining
• Market basket analysis
• Projects: customer segmentation, mall customer clustering, product recommendation basics and market basket analysis

• Importance of feature engineering
• Creating new features
• Removing unnecessary features
• Handling categorical and numerical variables
• Feature scaling
• Feature selection
• Correlation-based feature removal
• Recursive feature elimination
• PCA for feature reduction
• Improving model accuracy using better features

• What is model deployment?
• Saving machine learning models
• Pickle and Joblib
• Creating prediction pipeline
• Flask basics
• Streamlit basics
• FastAPI introduction
• Building simple ML web app
• Deploying model locally
• Cloud deployment overview
• API testing
• Projects: house price prediction app, salary prediction app, loan approval app and customer churn app

• Introduction to deep learning
• Neural network basics
• Perceptron
• Activation functions
• Loss functions
• Optimizers
• Gradient descent
• Backpropagation
• Artificial Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks basics
• TensorFlow basics
• Keras basics
• Projects: handwritten digit recognition, image classification, sentiment analysis and customer churn using neural network

• What is NLP?
• Text preprocessing
• Tokenization
• Stop word removal
• Stemming
• Lemmatization
• Bag of Words
• TF-IDF
• Word embeddings basics
• Sentiment analysis
• Text classification
• Spam detection
• Named Entity Recognition basics
• Chatbot basics
• Projects: email spam detection, review sentiment analysis, resume screening system and news category classification

• What is time series data?
• Trend, seasonality and noise
• Moving average
• Exponential smoothing
• Stationarity
• ARIMA basics
• Forecasting concepts
• Sales forecasting
• Stock price trend analysis basics
• Projects: monthly sales forecasting, website traffic forecasting and product demand forecasting

• Introduction to big data
• Volume, velocity and variety
• Hadoop basics
• Spark basics
• PySpark introduction
• Cloud computing basics
• AWS basics for data science
• Google Cloud basics
• Azure basics
• Cloud storage
• Data lake concept
• Data warehouse concept

• Introduction to Generative AI
• Large Language Models
• Prompt engineering basics
• ChatGPT for data analysis
• AI tools for productivity
• Using AI for code explanation
• Using AI for data cleaning ideas
• Using AI for report writing
• Limitations of AI tools
• Ethics and responsible AI
• Bias in AI models
• Data privacy

• Problem understanding
• Data collection
• Data cleaning
• Exploratory Data Analysis
• Feature engineering
• Model selection
• Model training
• Model evaluation
• Model improvement
• Model deployment
• Report creation
• Business presentation
• Documentation: problem statement, dataset description, tools used, model results, business conclusion and future scope

• Retail sales analysis
• Banking loan prediction
• Healthcare disease prediction
• HR employee attrition
• E-commerce customer behavior
• Marketing campaign analysis
• Financial fraud detection
• Education performance analysis
• Real estate price prediction
• Social media sentiment analysis
• KPI identification
• Dashboard planning
• Client communication and report presentation

• Data Science resume format
• Fresher resume preparation
• Project-based resume writing
• GitHub profile setup
• LinkedIn profile optimization
• Portfolio website guidance
• Python interview questions
• SQL interview questions
• Statistics interview questions
• Machine Learning interview questions
• Power BI interview questions
• Project explanation practice
• HR interview preparation
• Mock interviews
• Aptitude and logical reasoning basics