Best Full Stack Data Science Course

• Introduction to Python
• Operators
• String, List and Tuple
• Set and Dictionary
• Conditional statements and loops
• Functions in Python
• OOPs
• Polymorphism and Encapsulation
• Exception Handling, File Handling and Debugging
• Modules & Packages
• Regular Expressions
• Decorators and Generators
• Comprehension in Python

1 – Introduction to Data Science

• Fundamentals of data science
• Data science vs. data analysis vs. data engineering
• Descriptive and predictive analytics
• The data science life cycle
• Working with different data types: structured, semi-structured, and unstructured
• Applications of data science across various industries

2 – Data Acquisition:

• Beautiful Soup
• Scrapy

3 – Data Manipulation:

• Numpy
• Pandas

4 – Data Visualization:

• Matplotlib
• Seaborn

• Probability concepts
• Statistical measures
• Joint probability
• Conditional probability and Bayes theorem
• Measures of location and variability
• Probability distributions
• Sampling methods
• Key statistical concepts
• Statistical hypothesis testing
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1- Machine learning – Regression

• Machine learning basics and types
• Deep learning and recommender systems
• Regression concepts
• Univariate and multivariate linear regression
• Feature scaling
• Linear regression with scikit-learn
• Regularization (Lasso, Ridge, ElasticNet)
• Support vector regression
• Nearest neighbour regression
• Decision tree regression
• Feature engineering and categorical variables encoding
• Numerical variables transformation
• Feature selection (wrapper and intrinsic methods)
• Model evaluation metrics
• Dummy regressors
• Cross-validation

2 – Machine learning – Classification

• Types of classification problems
• Logistic regression
• Support vector machines
• Decision trees
• Naive Bayes
• K-nearest neighbors
• Ensemble learning
• XGBoost, LightGBM, and CatBoost
• Learning curves
• Model evaluation
• Dummy estimators
• Handling imbalanced class problems
• Hyperparameter optimization

3 – Machine learning – Clustering

• Unsupervised learning
• K-means clustering
• Hierarchical clustering
• DBSCAN clustering and customer segmentation
• Apriori algorithm and association rules
• Principal component analysis for dimensionality reduction
• Semi-supervised learning techniques

4 –  Machine Learning Tools and Libraries

• Automated machine learning with Pandas Profiling and PyCaret
• RAPIDS (Using GPU for Fast Computations

5 – Big Data Tools and Technologies

• What is big data?
• Hadoop ecosystem
• MapReduce framework
• Apache Spark and its components

• Data preprocessing with scikit-learn
• Data modeling with scikit-learn
• Clustering with scikit-learn
• Gradient boosting with XGBoost

• Introduction to deep learning
• Neural networks
• Feedforward neural networks
• Backpropagation
• Convolutional neural networks
• Recurrent neural networks

LSTM networks

• Introduction to TensorFlow and Keras
• Building and training deep learning models
• Evaluating and optimizing deep learning models

Introduction to MySQL and Relational Databases

• Overview of Databases and Data Analytics
• Introduction to MySQL and its role in data analytics
• Understanding Relational Databases
• Installation of MySQL
• Basic MySQL command-line interface (CLI) commands
• MySQL Data Types

Data Types, Table Creation, and CRUD Operations

• CRUD Operations
• Creating a Database and Tables
• Primary Keys and Foreign Keys
• Inserting Data into Tables
• Retrieving Data using SELECT statement
• Data Manipulation and Aggregation
• Filtering Data using WHERE clause
• Sorting Data using ORDER BY
• Using Aggregate Functions (COUNT, SUM, AVG, MAX, MIN)
• GROUP BY clause for grouping data
• HAVING clause for filtering grouped data
• Advanced SELECT statements
• Combining multiple queries

Joins, Relationships, and Subqueries

• Understanding Relationships in Databases
• INNER JOIN, LEFT JOIN, RIGHT JOIN
• Joining multiple tables
• Aliases for Tables
• Practical examples of using Joins
• Understanding Subqueries
• Using Subqueries in SELECT, WHERE, and FROM clauses

Advanced Queries and Functions

• Working with string functions: CONCAT, LENGTH, UPPER, LOWER.
• Date and time functions: DATE, TIME, TIMESTAMP.
• Conditional statements: CASE WHEN.
• Views
• Stored procedures and Functions

Database Design, Normalization, and Advanced Functions

• Introduction to database design principles
• Understanding normalization and its importance
• Exploring different normalization forms (1NF, 2NF, 3NF)

Data Analysis and Optimization

• Introduction to Data Analysis in MySQL
• Generating Reports with GROUP BY and Aggregate Functions
• Exporting Data to CSV or other formats
• Understanding indexes and their role in query performance
• Creating and managing indexes
• EXPLAIN statement: Analyzing query execution plans

Introduction to Excel Basics

• Excel Interface: Cells, rows, columns.
• Entering Data and Basic Formatting.
• Saving and Opening Files.
• Cell Referencing: Relative, Absolute, Mixed.
• Inserting and Deleting Rows and Columns.

Excel Functions and Common Operations

• Basic Functions: SUM, AVERAGE, COUNT, MIN, MAX.
• COUNT, COUNTA, COUNTBLANK,
• Advanced Mathematical Functions: PRODUCT, MOD, SQRT, and FACT.
• Rounding Functions: ROUND, ROUNDUP, and ROUNDDOWN.
• Handling Common Cell Errors.
• Data Validation
• Conditional and Number Formatting
• Text to columns and Flash fill options
• Find and Search: Locating and Replacing Data.
• Paste Special: Copying with Formatting and Operations.

Text Functions and Formulas

• Text Functions: LEFT, RIGHT, MID, LEN, CONCATENATE.
• Text Manipulation: REPLACE and SUBSTITUTE.
• Using TEXT and VALUE Functions.
• Date and Time Functions: TODAY, NOW, DATE, TIME.
• Using Logical Functions: IF, AND, OR, NOT, ISERROR, ISNUMBER, ISBLANK, ISTEXT.
• SUMIFS Function, COUNTIFS, AVGIF, MAXIF and MINIF Functions.

Nested IF Statements for Complex Conditions.

• Making Logical Decisions with IF Function.
• Handling Errors with IFERROR Function.
• Basic Statistical Functions: MEAN, MEDIAN, MODE, and STDEV.

Data Analysis and Visualization

• Chart Types
• Working with Pivot Tables

Advanced Techniques and Automation

• Introduction to Macros: Recording and Running Macros.
• Basic Introduction to VBA: Variables, Loops, Procedures.
• Creating Interactive Dashboards using Advanced Excel Techniques.

Introduction to Power BI

• Overview of Power BI and its applications
• Installing Power BI Desktop.
• Getting familiar with the Power BI interface.
• Connecting to data sources (e.g., Excel).

Power BI Charts with Formatting

• Basic Charts: Creating bar charts, line charts, and scatter plots.
• Applying basic formatting to charts.
• Using data labels and legends.
• Introducing pie charts and donut charts.
• Creating maps: Plotting geographical data on maps.
• Using tables: Display Data in Tabular Format
• Applying conditional formatting to highlight data.
• Introduction to filters and slicers for interactive filtering.
• Utilizing cards to display key metrics.
• Waterfall charts, funnel charts, and combination charts.
• Implementing scrollable visualizations for large datasets.
• Tree maps

Advanced Visualizations

• Creating info graphics: Using custom visuals and shapes.
• Inserting objects like images and buttons.
• Applying bookmark actions for interactive storytelling.
• Implementing advanced analytics features
• Constructing a complete report: Combining visualizations, text, and images.
• Formatting and arranging elements for a polished report.

Power Query Functions

• PQ Text Functions
• PQ Date Functions
• PQ Number Functions
• File Handling Techniques in Power Query

Introduction to M Language Power Query

• Appending Files and Tables in Power BI
• Merging Tables in Power Query (Power BI)
• Fill down and Fill up
• Grouping
• Transpose
• Unpivot
• Data Types in Power Query (Power BI)
• Replace Errors and Values
• Keep Rows and Remove Rows
• Add, Remove, Goto Columns

DAX Expressions

• Introduction to DAX (Data Analysis Expressions) language.
• Measures, Calculated Columns and Calculated Tables
• DAX Formulas

Power BI Data Modelling & Integration

• Connecting to MySQL database.
• Data normalization: Understanding 1NF, 2NF, and 3NF
• Refreshing data in Power BI and scheduling automatic refresh.
• Advanced Data Modelling
• OLTP vs. OLAP: Online Transaction Processing vs. Online Analytical Processing.
• Incorporating Python Scripts Advanced Analytics.

Power BI Publishing

• Publishing to Power BI Service: Uploading reports to the cloud.