Module 1: Introduction To Data Science, Analytics, Machine Learning & Artificial Intelligence

• Overview of Data Science, Analytics, Machine Learning & Artificial Intelligence
• Introduction to key concepts and definitions
• Understanding the role and significance of data in modern applications
• Exploring data sources and types
• Basic statistical analysis and visualization techniques
• Introduction to Machine Learning
• Real-world Examples of Practical applications in various industries
• Introduction to relevant tools, libraries, and programming languages
• Python Installation and basic usage capabilities

Module 1: Git & GitHub (Version Control Systems)

This course offers a comprehensive introduction to Git, a version control system, and GitHub, a popular platform for collaborative software development. Learn to effectively share and store work using these tools.

Introduction to Version Control Systems

• Overview of version control systems
• Benefits of using Git for version control

Git Basics

• Installation and setup of Git
• Initializing a Git repository
• Understanding the Git workflow: staging, committing, and branching
• Managing and navigating Git history

Working with Git Remotely

• Introduction to remote repositories
• Cloning a repository from GitHub
• Pushing and pulling changes to/from remote repositories

Collaborating with GitHub

• Introduction to GitHub and its features
• Forking and cloning repositories
• Creating and managing branches
• Pull requests and code review

Module 2: LinkedIn Profile building

This course provides a comprehensive guide to optimizing your LinkedIn profile for professional success and networking opportunities.

• Introduction to LinkedIn and Personal Branding
• Profile Basics
• Showcasing Skills and Accomplishments
• Increasing Visibility and Engagement
• Leveraging LinkedIn Features
• Best Practices for Profile Optimization

Python is a versatile programming language widely used in data analytics and data science. With its rich libraries and frameworks like NumPy, Pandas, and scikit-learn, Python enables efficient data manipulation, analysis, and modelling, making it an essential tool for extracting insights from data.

Module 1: Core Python Programming

• Python Environment Setup
• Basic operations in Python
• Introduction to 14 data types of Python
• Numeric Data Types with modules
• Operators in Python
• Decision & Loop Controls
• Project: Build a simple calculator

Module 2: Data Structures & Algorithms in Python List, Tuples & Sets

• Dictionary and Hashing
• Strings & Regular Expressions
• Stacks and Queues
• Linked List
• Trees and Binary Search Trees
• Sorting and Searching Algorithms
• Project: Implement a contact management system

Module 3: Advance Python Programming

• Functions & Modules
• Lambda Functions
• Regular Expressions (RegEx)
• File Handling and Input/Output
• Exception Handling & Custom Exceptions
• Generators & Decorators

Module 4: Web Scraping using Python

• Introduction to Web Scraping
• Introduction to Web Requests & HTTP
• Parsing HTML with Beautiful Soup
• Project: Scrape and Analyze Data from a Website

Module 5: OOPs in Python

• Understanding Classes and Objects
• Encapsulation, Inheritance, and Polymorphism
• Abstraction and Interfaces
• Method Overriding and Overloading
• Class Variables and Instance Variables

Module 6: Python For Analytics

NumPy

• Introduction to NumPy arrays and operations
• Array indexing and slicing
• Mathematical functions and statistical operations
• Array reshaping and manipulation
• Linear algebra with NumPy
• Introduction to NumPy broadcasting

Pandas

• Introduction to Pandas data structures (Series and DataFrame)
• Data cleaning and preprocessing techniques
• Data exploration and manipulation using Pandas
• Handling missing data and outliers
• Aggregating and summarizing data
• Merging and joining datasets in Pandas

Matplotlib

• Introduction to Matplotlib and its plotting capabilities
• Creating line plots, scatter plots, bar plots, and histograms
• Customizing plot aesthetics and adding annotations
• Creating subplots and multiple axes
• Plotting with categorical variables
• Visualizing trends and patterns in data using Matplotlib

Seaborn

• Introduction to Seaborn and its statistical visualization capabilities
• Creating various types of plots such as scatter plots, box plots, and violin plots
• Customizing plot aesthetics and colour palettes
• Visualizing relationships between variables with regression plots and heatmaps
• Creating facet grids for multi-plot visualizations
• Exploring advanced visualization techniques in Seaborn

EDA Project

Analyze Data to Gain Insights and Identify Patterns – Use concepts like Remove duplicates, handle missing values, Calculate basic statistics like mean, median, and standard deviation to summarize the data. Create charts and graphs to visualize trends, patterns, and data behaviour.

Tools: Python: Jupyter Notebook – Pandas, NumPy, Matplotlib and Seaborn for analysis

Statistics and Machine Learning involve analyzing and interpreting data to gain insights and make predictions. Statistics focuses on data description, inference, and hypothesis testing, while Machine Learning involves developing algorithms and models to learn patterns and make predictions from data.

Module 1: Statistics & Probability

• Introduction to Statistics: Descriptive and inferential statistics, types of data
• Probability Theory: Probability rules, random variables, probability distributions
• Sampling and Estimation: Sampling techniques, point and interval estimation
• Hypothesis Testing: Null and alternative hypotheses, significance level, p-value
• Regression Analysis: Simple and multiple linear regression, model fitting and interpretation
• Analysis of Variance (ANOVA): One-way and two-way ANOVA, post-hoc tests
• Non-parametric Methods: Chi-square test, Mann-Whitney U test, Wilcoxon signed-rank test

Module 2: Machine Learning

• Introduction to Machine Learning: Supervised and unsupervised learning, model evaluation
• Linear Models: Linear regression, logistic regression, regularization techniques like lasso and ridge regression
• Evaluation metrics for regression and classification models
• Decision Trees and Random Forests: Tree-based models, ensemble methods with regression and classification projects
• Support Vector Machines (SVM): Linear and nonlinear SVM, kernel methods with regression and classification projects
• Clustering Algorithms: K-means, hierarchical clustering, DBSCAN
• Dimensionality Reduction: Principal Component Analysis (PCA), feature selection
• Other models: K-NN, Naive Bayes’, Boosting Algorithms – AdaBoost, CatBoost, XGBoost
• Model Evaluation and Validation: Cross-validation, performance metrics, overfitting, bias-variance tradeoff
• Model Selection and Tuning: Grid search, hyperparameter optimization, model deployment

Time-Series Data Analysis involves studying data points collected over time to uncover patterns, trends, and seasonality. It is used in forecasting and predicting future values. Text Data Analysis involves processing and extracting insights from unstructured textual data, such as sentiment analysis, topic modelling, and text classification.

Module 1: Time-Series Data Analysis

• Introduction to time series data
• Time series visualization and exploration
• Time series decomposition
• Stationarity and its tests
• Autocorrelation and partial autocorrelation analysis
• ARIMA models for time series forecasting
• Seasonal ARIMA models (SARIMA)
• Exponential smoothing methods
• Evaluating time series models

Module 2: Text Data Analysis (Natural Language Processing)

• Introduction to NLP and text data
• Text preprocessing techniques (tokenization, stemming, lemmatization, etc.)
• Bag-of-Words model and TF-IDF
• Text classification using machine learning algorithms (Naive Bayes, SVM, etc.)
• Sentiment analysis using NLP techniques
• Topic modeling (LDA, LSA)
• Named Entity Recognition (NER)
• Word embeddings (Word2Vec, GloVe)

Reinforcement Learning is a subfield of machine learning where an agent learns to make sequential decisions by interacting with an environment. Deep Learning, on the other hand, is a subset of machine learning that focuses on using artificial neural networks to model and understand complex patterns in data.

Module 1: Reinforcement Learning

• Introduction to Reinforcement Learning
• Markov Decision Processes
• Dynamic Programming
• Monte Carlo Methods
• Temporal Difference Learning
• Q-Learning and SARSA
• Function Approximation in Reinforcement Learning
• Policy Gradient Methods
• Deep Q-Networks (DQN)
• Advanced Topics in Reinforcement Learning

Module 2: Deep Learning & Computer Vision

• Introduction to Deep Learning
• Artificial Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Generative Models (e.g., Variational Autoencoders, Generative Adversarial Networks)
• LSTM (Long short-term memory)
• Transfer Learning and Fine-tuning
• Optimization Techniques for Deep Learning
• Hyperparameter Tuning
• Advanced Architectures (e.g., Transformers, Capsule Networks)
• Explainability and Interpretability in Deep Learning
• Image Classification
• Object Detection
• Semantic Segmentation
• Face Recognition
• Sentiment Analysis

Database management involves the storage, organization, and retrieval of data. Structured databases use predefined schemas and are suitable for tabular data, while unstructured databases store data in flexible formats like text, images, and multimedia. Both are essential for efficient data management in various applications.

Module 1: SQL – Structured Database Management System

Introduction to SQL

SQL Basics

Advanced SQL Queries

• • Joins, subqueries and nested queries
  • Aggregating data, functions and expressions
  • Modifying data & Creating views

Database Design and Normalization

Advanced Database Concepts

• • Indexing and query optimization
  • Stored procedures, functions, and triggers
  • User-defined types and objects

Module 2: MongoDB – Unstructured Database Management System

Introduction to MongoDB

MongoDB Basics

Advanced MongoDB Queries

• • Indexing and query optimization
  • Aggregation, joins, geospatial queries
  • Using text search and full-text search indexes

MongoDB Administration

• • Managing user access and roles
  • Sharding for horizontal scaling

MongoDB and Programming Languages

• • Integrating MongoDB with Python programming languages 
  • CRUD operations using programming languages

Data Visualization is presenting data in visual formats such as charts, graphs, and maps to facilitate understanding and gain insights. We can use various tools. This course covers 3 of the most popular data visualization & analytics tools to add in your arsenal: Power BI, Tableau & Excel for Data Analytics

Module 1: Power BI

Introduction to Power BI

Data Preparation and Modeling

Data Visualization Techniques

• • Different types of visualizations
  • Creating interactive and dynamic visualizations
  • Formatting and customizing visual elements
  • Utilizing slicers and filters for data exploration

Advanced Analytics in Power BI

• • Implementing advanced calculations using DAX expressions
  • Incorporating statistical functions and forecasting
  • Utilizing advanced visuals and custom visuals
  • Applying business intelligence best practices

Power BI Sharing and Collaboration

Module 2: Tableau

Introduction to Tableau

Data Preparation and Transformation

Building Visualizations in Tableau

• • Creating charts, graphs, and maps
  • Utilizing marks, dimensions, and measures
  • Implementing filters and sets for data exploration
  • Adding interactivity and drill-down functionality

Advanced Analytics in Tableau

• • Implementing calculations using Tableau’s calculation language
  • Incorporating statistical functions and forecasting
  • Working with parameters and input controls
  • Creating advanced visualizations (e.g., heat maps, tree maps)

Tableau Dashboards and Storytelling

Tableau Sharing and Collaboration

Module 3: Excel for Analytics

Introduction to Excel for Analytics

Data Preparation and Cleaning

Data Analysis Techniques in Excel

• • Exploring statistical analysis functions in Excel
  • Using PivotTables and PivotCharts for data summarization
  • Performing what-if analysis and goal-seeking
  • Applying data visualization techniques in Excel

Advanced Excel Analytics

• • Implementing advanced functions and formulas (e.g., INDEX, MATCH, VLOOKUP)
  • Utilizing Excel’s Power Query and Power Pivot for data modelling
  • Incorporating Excel’s data analysis add-ins (e.g., Solver, Analysis ToolPak)

The process of collecting, storing, and processing large volumes of data from various sources to extract valuable insights. Tools like Hadoop, Spark, and Kafka enable efficient handling, analysis, and integration of big data for decision-making and data-driven applications.

Module 1: Apache Hadoop

• Introduction to Apache Hadoop
• Hadoop Distributed File System (HDFS)
• Hadoop MapReduce
• Hadoop Ecosystem Tools – Hive, Pig, HBase, Sqoop
• Hadoop Administration and Monitoring

Module 2: Apache Spark

• Introduction to Apache Spark
• Spark Core
• Spark Streaming
• Spark Machine Learning Library (MLlib)
• Spark Graph Processing (GraphX)

The process of deploying machine learning models and data science applications on cloud platforms, enabling scalable and accessible solutions for real-time predictions and insights.

Module 1: AWS

• Introduction to AWS
• Setting Up AWS Environment
• Model Deployment using AWS SageMaker / Lambda / ECS and Docker

Module 2: Azure

• Introduction to Azure
• Describe AI workloads & considerations
• Fundamental Principles of Machine Learning on Azure
• Describe features of computer vision workloads on Azure
• Describe features of natural language processing (NLP) workloads on Azure
• Azure Machine Learning Studio
• Azure Functions for Serverless Deployment
• Model Deployment with Azure Container Instances

Module 3: Heroku

• Introduction to Heroku
• Deploying Machine Learning Models with Heroku
• Monitoring and optimizing the deployed application

Reinforcement Learning is a subfield of machine learning where an agent learns to make sequential decisions by interacting with an environment. Deep Learning, on the other hand, is a subset of machine learning that focuses on using artificial neural networks to model and understand complex patterns in data.

Module 1: Agile & Scrum

• Introduction to Agile Project Management
• Understanding the Agile methodology and principles
• Scrum Framework
• Daily Scrum and Task Management
• Sprint Review and Retrospective
• Agile Project Planning
• Agile Execution and Monitoring
• Agile Metrics and Reporting
• Agile Project Adaptation and Continuous Improvement

Module 2: Jira

• Introduction to Jira
• Configuring Jira for Data Science Projects
• Creating and Managing Projects
• Task Management and Collaboration in Jira
• Reporting and Dashboards in Jira
• Integrating Jira with other Tools and Systems