• Spoken English
• Aptitude & Reasoning
• Data Structures
• Java Programming
• Python Basics
• Python for Data Science
• Maths for Data Science
• Probability and Statistics
• Applied Data Science
• Databases and SQL
• Data Acquisition and Unstructured Data
• Tableau Desktop and Server
• Business Product Strategy
• Microsoft Excel
• R Programming
• Power BI
• Machine Learning for Data Analytics
• Business Awareness
• Machine Learning for Data Science
• Artificial Intelligence
• Machine Learning and Operations (MLOps))
• Git and Git Hub
• Agile Scrum

1) Basic Grammar Knowledge

• Parts of the speech
• Types of the sentences
• 12 active voice tenses.
• 8 passive voice tenses.
• Use of articles.
• Use of prepositions.
• Modal auxiliaries.
• Clauses
• Direct and indirect speech.
• Some special constructions like feel like going to, had better, used to, as if, be, get, prefer to.

2) Essential English Vocabulary

• Verbs
• Nouns
• Adjectives
• Adverbs
• Prepositions
• Interjections
• Conjunctions

3) Speaking practice

• Self-introduction.
• Daily routine.
• My family.
• My village/city.
• My school.
• My hobby.
• My favorites.

1. Quantitative Ability (Basic Mathematics)

• Number Systems
• LCM and HCF
• Decimal Fractions
• Simplification
• Square Roots and Cube Roots
• Average
• Problems on Ages
• Surds & Indices
• Percentages
• Problems on Numbers

2. 
   Quantitative Ability (Applied & Engineering Mathematics)

• Logarithm
• Permutation and Combinations
• Probability
• Profit and Loss
• Simple and Compound Interest
• Time, Speed and Distance
• Time & Work
• Ratio and Proportion
• Area
• Mixtures and Allegation

3. Data Interpretation

• Data Interpretation
• Tables
• Column Graphs
• Bar Graphs
• Line Charts
• Pie Chart
• Venn Diagrams

4. Logical Reasoning (Deductive Reasoning)

• Analogy
• Blood Relation
• Directional Sense
• Number and Letter Series
• Coding – Decoding
• Calendars
• Clocks
• Venn Diagrams
• Seating Arrangement
• Syllogism
• Mathematical Operations

DS Basics

• DS Introduction
• DS Algorithm
• Ds Asymptotic Analysis
• DS Pointer
• DS Structure

DS Array

• Array
• 2D Array

DS Linked List

• Linked List
  • Insertion at beginning
  • Insertion at end
  • Insertion after specified node
  • Deletion at beginning
  • Deletion at end
  • Deletion after specified node
  • Traversing
  • Searching
• Doubly Linked List
  • Insertion at beginning
  • Insertion at end
  • Insertion after specified node
  • Deletion at beginning
  • Deletion at end
  • Deletion of node having given data
  • Traversing
  • Searching
• Circular Linked List
  • Insertion at beginning
  • Insertion at end
  • Deletion at beginning
  • Deletion at the end
  • Traversing
  • Searching
• Circular Doubly List
  • Insertion at beginning
  • Insertion at end
  • Deletion at beginning
  • Deletion at the end

DS Stack

• DS Stack
• Array Implementation
• Linked List Implementation

DS Queue

• DS Queue
• Array Implementation
• Linked List Implementation
• Circular Queue

DS Tree

• Tree
• Binary Tree
  • Pre-order Traversal
  • In-order Traversal
  • Post-order Traversal
• Binary Search Tree
  • Searching in BST
  • Insertion in BST
  • Deletion in BST
• AVL Tree
  • Insertion in AVL Tree
    • LL Rotation
    • LR Rotation
    • RL Rotation
    • RR Rotation
  • Deletion in AVL Tree
• B Tree
• B+ Tree
• Red Black Tree

DS Graph

• DS Graph
• Graph Implementation
• BFS Algorithm
• DFS Algorithm
• Spanning Tree
  • Prim’s Algorithm
  • Kruskal’s Algorithm

DS Searching

• Linear Search
• Binary Search

DS Sorting

• Bubble Sort
• Bucket Sort
• Comb Sort
• Counting Sort
• Heap Sort
• Insertion Sort
• Merge Sort
• Quick Sort
• Radix Sort
• Selection Sort
• Shell Sort
• Bitonic Sort
• Cocktail Sort
• Cycle Sort
• Tim Sort

What is Java

History of Java

Features of Java

C++ vs Java

Hello Java Program

Program Internal

How to set path?

JDK, JRE and JVM

JVM: Java Virtual Machine

Java Variables

Java Data Types

Unicode System

Operators

Keywords

Control Statements

Java Control Statements

Java If-else

Java Switch

Java For Loop

Java While Loop

Java Do While Loop

Java Break

Java Continue

Java Comments

Java Programs

Java Object Class

Java OOPs Concepts

Naming Convention

Object and Class

Method

Constructor

static keyword

this keyword

Java Inheritance

Inheritance(IS-A)

Aggregation(HAS-A)

Java Polymorphism

Method Overloading

Method Overriding

Covariant Return Type

super keyword

Instance Initializer block

final keyword

Runtime Polymorphism

Dynamic Binding

instance of operator

Java Abstraction

Abstract class

Interface

Abstract vs Interface

Java Encapsulation

Package

Access Modifiers

Encapsulation

Java Array

Java Array

Java OOPs Misc

Object class

Object Cloning

Math class

Wrapper Class

Java Recursion

Call By Value

strictfp keyword

javadoc tool

Command Line Arg

Object vs Class

Overloading vs Overriding

Java String

Java Regex

Exception Handling

Java Inner classes

Java Multithreading

Java I/O

Java Networking

Java AWT & Events

Java Swing

JavaFX

Java Applet

Java Reflection

Java Date

Java Conversion

Java Collection

Java JDBC

Java Misc

Java New Features

RMI 

Internationalization

• Flowcharts, Data Types, Operators
• Conditional Statements & Loops
• Functions & Recursion
• Strings
• In-built Data Structures – List, Tuple, Dictionary, Set
• Practice
• Python Refresher
• Lambda Functions, List Comprehension, Functional Programming, Decorator, Args, Kwargs
• Object Oriented Programming
• Exception Handling, Modules, Package, Library, Built-in Modules in Python
• Basic DSA & Problem Solving
• Time complexity, List, 2D List, Bit Manipulation, Strings, Searching, Sorting

• Numpy, Pandas
• Data Visualization using Matplotlib and Seaborn
• Regular Expressions/Pattern Matching
• Terminal/OS
• File Handling

• Coordinate Geometry
  • Point, Lines, Slope, Intercept
• Linear Algebra
  • Vector and Matrices, Unit Vector, Dot product, Projections, Cosine Similarity, Determinant, Transpose, System of Equations
• Linear Programming Optimisation Basics
• Estimation Problems

• Probability Theory and Descriptive Statistics
  • Combinatorics, Marginal Probability, Joint Probability, Conditional Probability, Bayes Theorem, Mean, Median, Mode, Percentile, IQR, Outlier.
• Probability Distributions
  • DF, PMF, CDF, PPF, Uniform, Gaussian, Bernoulli, Multinomial, Normal Distribution, Poisson, Exponential, Geometric, Log-normal distribution, Pareto/Power Law Distribution.
• Inferential Statistics
  • Confidence Interval Estimates, CLT.

• Hypothesis Testing, Parametric vs non-parametric, Z-test, Chi-square, Skewness, Kurtosis, Normality test.
• Experiment Design, ANOVA, Simulations, Power of Test, A/B testing, Diff n Diff, Multi-arm bandits.
• EDA, Covariance, Correlation, Pearson, Spearman Rank, Multi-dimensional, Feature Engineering, Column normalization, Standardization, Covariance matrix, Missing Values, Outlier treatment.

• Relational, Non-relational, ER diagrams, SQL Commands, Aggregate Functions, Joins, SubQueries, Normalization, Scaling patterns, ACID, Dask SQL, Cloud SQL (Athena/BigQuery)

• Data from Web API, Scraping, Data Cleaning
• Unstructured Data – OpenCV, Image processing, Smoothening, Morphological Operations, NLTK, Text processing

Tableau Desktop

• Introduction Tableau
• Connecting to Excel, CSV Text Files
• Getting Started
• Product Overview
• Connecting to Databases
• Working with Data
• Analyzing
• Formatting
• Introduction to Calculations
• Dashboard Development
• Sharing
• Data Calculations
• Aggregate Calculations
• User Calculations
• Table Calculations
• Logical Calculations
• String Calculations
• Number Calculations
• Type Conversion
• Parameters
• Filtering Conditions
• Filtering Measures
• Histograms
• Sorting
• Grouping
• Sets
• Tree maps, word clouds and bubble charts
• Pareto Charts
• Waterfall Charts
• Bump Charts
• Funnel Charts
• Bollinger Bands

Tableau Server

• Install Configuration
• Tab admin
• Tab cmd
• Data Server
• End User Training
• JavaScript API Intro and Embed
• JavaScript API Switching Views
• JavaScript API Filtering and Selecting
• JavaScript API Asynchronous Programming
• JavaScript API Event Listeners
• JavaScript API Advanced Filtering
• JavaScript API Utility Function

Tableau Advanced

• Authoring for Interactivity
• Data Blending
• Basic Mapping
• Advanced Mapping Techniques
• WMS Servers
• Polygon Maps
• Background Images
• Custom Geo coding
• Cubes
• Trend Lines, Residuals, and Forecasting
• Statistics Calculations
• Ben ford’s Law
• Box Plots
• Sales force
• Google Analytics
• Extract API CSV to TDE
• Connecting to Web-based Data Sources with the Extract API
• Extract API Transforming Your Data
• Analyzing Sales Data with Tableau
• Tableau Online Security and Administration
• Tableau Online Security and Administration
• Tableau Online Updating Data to the Cloud

Tableau Visual Analytics Training

• Introduction
• Laying the Groundwork for Visual Analysis
• Getting, Cleaning and Classifying Your Data
• Visual Mapping Techniques
• Solving Real-World Problems
• Communicating Your Findings

• Metric Design
• How to crack Product and Strategy Rounds
• Domain Knowledge – Banking, Finance, Marketing, Social Media, Operations, Healthcare
• Experiment Design Advanced

• • Getting Acclimated with EXCEL, Basic EXCEL Formulas & Functions , Text, Times, and Dates Data Formats, Pivot Table, Introduction to Statistics in EXCEL , Handling Data in EXCEL, Pivot Tables, Charts in Excel

• • Creating objects, Matrices, Loops, Functions in R, Data Manipulation in R, Creating frequency tables and cross tables, Building charts, Performing univariate analysis, Working with dates and time in R, Dplyr, GGplot

Introduction To Power BI

• Introduction to Power BI – Need, Imprtance

• Power BI – Advantages and Scalable Options

• History – Power View, Power Query, Power Pivot

• Power BI Data Source Library and DW Files

• Cloud Colloboration and Usage Scope

• Business Analyst Tools, MS Cloud Tools

• Power BI Installation and Cloud Account

• Power BI Cloud and Power BI Service

• Power BI Architecture and Data Access

• OnPremise Data Acces and Microsoft On Drive

• Power BI Desktop – Instalation, Usage

• Sample Reports and Visualization Controls

• Power BI Cloud Account Configuration

• Understanding Desktop & Mobile Editions

• Report Rendering Options and End User Access

• Power View and Power Map. Power BI Licenses

• Course Plan – Power BI Online Training

Creating POWER BI Reports, Auto Filters

• Report Design with Legacy & .DAT Files

• Report Design with Databse Tables

• Understanding Power BI Report Designer

• Report Canvas, Report Pages: Creation, Renames

• Report Visuals, Fields and UI Options

• Experimenting Visual Interactions, Advantages

• Reports with Multiple Pages and Advantages

• Pages with Multiple Visualizations. Data Access

• PUBLISH Options and Report Verification in Cloud

• “GET DATA” Options and Report Fields, Filters

• Report View Options: Full, Fit Page, Width Scale

• Report Design using Databases & Queries

• Query Settings and Data Preloads

• Navigation Options and Report Refresh

• Stacked bar chart, Stacked column chart

• Clustered bar chart, Clustered column chart

• Adding Report Titles. Report Format Options

• Focus Mode, Explore and Export Settings

Report Visualization And Properties

• Power BI Design: Canvas, Visualizations and Files

• Import Data Options with Power BI Model, Advantages

• Direct Query Options and Real-time (LIVE) Data Access

• Data Fields and Filters with Visualizations

• Visualization Filters, Page Filters, Report Filters

• Conditional Filters and Clearing. Testing Sets

• Creating Customized Tables with Power BI Editor

• General Properties, Sizing, Dimensions, and Positions

• Alternate Text and Tiles. Header (Column, Row) Properties

• Grid Properties (Vertical, Horizontal) and Styles

• Table Styles & Alternate Row Colors – Static, Dynamic

• Sparse, Flashy Rows, Condensed Table Reports. Focus Mode

• Totals Computations, Background. Borders Properties

• Column Headers, Column Formatting, Value Properties

• Conditional Formatting Options – Color Scale

• Page Level Filters and Report Level Filters

• Visual-Level Filters and Format Options

• Report Fields, Formats and Analytics

• Page-Level Filters and Column Formatting, Filters

• Background Properties, Borders and Lock Aspect

Chart And Map Report Properties

• CHART Report Types and Properties

• STACKED BAR CHART, STACKED COLUMN CHART

• CLUSTERED BAR CHART, CLUSTERED COLUMN CHART

• 100% STACKED BAR CHART, 100% STACKED COLUMN CHART

• LINE CHARTS, AREA CHARTS, STACKED AREA CHARTS

• LINE AND STACKED ROW CHARTS

• LINE AND STACKED COLUMN CHARTS

• WATERFALL CHART, SCATTER CHART, PIE CHART

• Field Properties: Axis, Legend, Value, Tooltip

• Field Properties: Color Saturation, Filters Types

• Formats: Legend, Axis, Data Labels, Plot Area

• Data Labels: Visibility, Color and Display Units

• Data Labels: Precision, Position, Text Options

• Analytics: Constant Line, Position, Labels

• Working with Waterfall Charts and Default Values

• Modifying Legends and Visual Filters – Options

• Map Reports: Working with Map Reports

• Hierarchies: Grouping Multiple Report Fields

• Hierarchy Levels and Usages in Visualizations

• Preordered Attribute Collection – Advantages

• Using Field Hierarchies with Chart Reports

• Advanced Query Mode @ Connection Settings – Options

• Direct Import and In-memory Loads, Advantages

Hierarchies And Drilldown Reports

• Hierarchies and Drilldown Options

• Hierarchy Levels and Drill Modes – Usage

• Drill-thru Options with Tree Map and Pie Chart

• Higher Levels and Next Level Navigation Options

• Aggregates with Bottom/Up Navigations. Rules

• Multi Field Aggregations and Hierarchies in Power BI

• DRILLDOWN, SHOWNEXTLEVEL, EXPANDTONEXTLEVEL

• SEE DATA and SEE RECORDS Options. Differences

• Toggle Options with Tabular Data. Filters

• Drilldown Buttons and Mouse Hover Options @ Visuals

• Dependant Aggregations, Independant Aggregations

• Automated Records Selection with Tabular Data

• Report Parameters : Creation and Data Type

• Available Values and Default values. Member Values

• Parameters for Column Data and Table / Query Filters

• Parameters Creation – Query Mode, UI Option

• Linking Parameters to Query Columns – Options

• Edit Query Options and Parameter Manage Entries

• Connection Parameters and Dynamic Data Sources

• Synonyms – Creation and Usage Options

Power Query & M Language

• Understanding Power Query Editor – Options

• Power BI Interface and Query / Dataset Edits

• Working with Empty Tables and Load / Edits

• Empty Table Names and Header Row Promotions

• Undo Headers Options. Blank Columns Detection

• Data Imports and Query Marking in Query Editor

• JSON Files & Binary Formats with Power Query

• JavaScript Object Notation – Usage with M Lang.

• Applied Steps and Usage Options. Revert Options

• creating Query Groups and Query References. Usage

• Query Rename, Load Enable and Data Refresh Options

• Combine Queries – Merge Join and Anti-Join Options

• Combine Queries – Union and Union All as New Dataset

• M Language : NestedJoin and JoinKind Functions

• REPLACE, REMOVE ROWS, REMOVE COL, BLANK – M Lang

• Column Splits and FilledUp / FilledDown Options

• Query Hide and Change Type Options. Code Generation

DAX EXPRESSIONS – Level 1

• Purpose of Data Analysis Expresssions (DAX)

• Scope of Usage with DAX. Usabilty Options

• DAX Context : Row Context and Filter Context

• DAX Entities : Calculated Columns and Measures

• DAX Data Types : Numeric, Boolean, Variant, Currency

• Datetime Data Tye with DAX. Comparison with Excel

• DAX Operators & Symbols. Usage. Operator Priority

• Parenthesis, Comparison, Arthmetic, Text, Logic

• DAX Functions and Types: Table Valued Functions

• Filter, Aggregation and Time Intelligence Functions

• Information Functions, Logical, Parent-Child Functions

• Statistical and Text Functions. Formulas and Queries

• Syntax Requirements with DAX. Differences with Excel

• Naming Conventions and DAX Format Representation

• Working with Special Characters in Table Names

• Attribute / Column Scope with DAX – Examples

• Measure / Column Scope with DAX – Examples

DAX EXPRESSIONS – Level 2

• YTD, QTD, MTD Calculations with DAX

• DAX Calculations and Measures

• Using TOPN, RANKX, RANK.EQ

• Computations using STDEV & VAR

• SAMPLE Function, COUNTALL, ISERROR

• ISTEXT, DATEFORMAT, TIMEFORMAT

• Time Intelligence Functions with DAX

• Data Analysis Expressions and Functions

• DATESYTD, DATESQTD, DATESMTD

• ENDOFYEAR, ENDOFQUARTER,ENDOFMONTH

• FIRSTDATE, LASTDATE, DATESBETWEEN

• CLOSINGBALANCEYEAR,CLOSINGBALANCEQTR

• SAMEPERIOD and PREVIOUSMONTH,QUARTER

• KPIs with DAX. Vertipaq Queries in DAX

• IF..ELSEIF.. Conditions with DAX

• Slicing and Dicing Options with Columns, Measures

• DAX for Query Extraction, Data Mashup Operations

• Calcualted COlumns and Calculated Measures with DAX

Power BI Deployment & Cloud

• PowerBI Report Validation and Publish

• Understanding PowerBI Cloud Architecture

• PowerBI Cloud Account and Workspace

• Reports and DataSet Items Validation

• Dashboards and Pins – Real-time Usage

• Dynamic Data Sources and Encryptions

• Personal and Organizational Content Packs

• Gateways, Subscriptions, Mobile Reports

• Data Refresh with Power BI Architecture

• PBIX and PBIT Files with Power BI – Usage

• Visual Data Imports and Visual Schemas

• Cloud and On-Premise Data Sources

• How PowerBI Supports Data Model?

• Relation between Dashboards to Reports

• Relation between Datasets to Reports

• Relation between Datasets to Dashboards

• Page to Report – Mapping Options

• Publish Options and Data Import Options

• Need for PINS @ Visuals and PINS @ Reports

• Need for Data Streams and Cloud Integration

Power BI Cloud Operations

• Report Publish Options and Verifications

• Working with Power BI Cloud Interface & Options

• Navigation Paths with “My Workspace” Screens

• FILE, VIEW, EDIT REPORTS, ACCESS, DRILLDOWN

• Saving Reports into pdf, pptx, etc. Report Embed

• Report Rendering and EDIT, SAVE, Print Options

• Report PIN and individual Visual PIN Options

• Create and Use Dashboards. Menu Options

• Goto Dashboard and Goto LIVE Page Options

• Operations on Pinned Reports and Visuals

• TITLE, MEDIA, USAGE METRICS & FAVOURITES

• SUBSCRIPTION Options and Reports with Mobile View

• Options with Report Page : Print and Subscribe

• Report Actions: USAGE METRICS, ANALYSE IN EXCEL

• Report Actions: RELATED ITEMS, RENAME, DELETE

• Dashboard Actions: METRICS, RELATED ITEMS

• Dashboard Actions: SETTINGS FOR Q & A, DELETE

• PIN Actions: METRICS, SHARE, RELATED ITEMS

• PIN Actions: SETTINGS FOR Q & A, DELETE

• EDIT DASHBOARD (CLOUD), On-The-Fly Reports

• Dataset Actions: CREATE REPORT, REFRESH

• SCHEDULED REFRESH & RELATED ITEMS

• Dashboard Integration with Apps in Power BI

Improving Power BI Reports

• Publish PowerBI Report Templates

• Import and Export Options with Power BI

• Dataset Navigations and Report Navigations

• Quick Navigation Options with “My Workspace”

• Dashboards, Workbooks, Reports, Datasets

• Working with MY WORK SPACE group

• Installing the Power BI Personal Gateway

• Automatic Refresh – Possible Issues

• Adding images to the dashboards

• Reading & Editing Power BI Views

• Power BI Templates (pbit)- Creation, Usage

• Managing report in Power BI Services

• PowerBI Gateway – Download and Installation

• Personal and Enterprise Gateway Features

• PowerBI Settings : Dataset – Gateway Integration

• Configuring Dataset for Manual Refresh of Data

• Configuring Automatic Refresh and Schedules

• Workbooks and Alerts with Power BI

• Dataset Actions and Refresh Settings with Gateway

• Using natural Language Q&A to data – Cortana

Insights And Subscriptions

• Data Navigation Paths and Data Splits

• Getting data from existing systems

• Data Refresh and LIVE Connections

• pbit and pbix : differences. Usage Options

• Quick Insights For Power BI Reports

• Quick Insights For PowerBI Dashboards

• Generating Insights with Cloud Datasets

• Generating Reports with Cloud Datasets

• Using relational databases on-premises

• Using relational databases in the cloud

• Consuming a service content pack

• Creating a custom data set from a service

• Creating a content pack for your organization

• Consuming an organizational content pack

• Updating an organizational content pack

• Adding Tiles : Images, Videos, DataStreams

• Creating New Reports from Cortana, Advantages

Power BI Integration Elements

• SSRS Integration with Power BI

• SSRS Report Portal URL to Power BI Cloud

• Power BI KPI Reports Vs SSRS KPI Reports

• Converging and Working with Mobile Reports

• Report Builder Reports to Power BI

• Generating QR Codes and Report Security

• Reporting JSON Files, Bulk Data Loads

• Creating high-density Reports in Power BI

• OLAP Data Sources in Power BI

• Using MDX Queries with PowerBI Queries

• MDX SELECT and Perspective Access

• KPIs and MDX Expressions with Power BI

• MDX Queries and Filters with Power BI

• Linked Servers and T-SQL SPROCs with MDX

• YTD, PARALLELPERIOD,SCOPE, ALLMEMBERS

• WHERE, EXCEPT, RANGE, NONEMPTY

• CURRENT & EMPTY, AND / OR, LEFT / RIGHT

• Implementing Row Level Security (RLS)

• Security Roles and Role Members. Tests

• Using R for Power BI, Streaming Data Sets

• Azure Connections with PowerBI Desktop

• PowerBI Reports using SQL Azure DBs

• Supervised Machine Learning
  • KNN, Linear Regression, Logistic Regression, Decision Trees, Feature importance, ML Metrics
• Unsupervised Machine Learning
  • Dimentionality Reduction & Visualization, Anomaly Detection, K-Means, PCA, t-SNE
• Miscellaneous Machine Learning Topics
  • Text and Image vectorization using Deep-Learning, Interpretable ML, ML Life Cycle
• NLP Concepts
• Time Series Forecasting
  • Resampling, Autocorrelation, Forecasting, Seasonal Naive, Double/Triple Exponential (Holt) Residual Analysis, Stationarity tests, Autoregressive methods, moving average, ARIMA, SARIMA.

• Design of Survey
• Metric Design
• Big Data Frameworks
• Business Case-studies
  • Risk, Product, Banking, Finance, E-Commerce, Social Media, Marketing, Transportation, Healthcare, Operations

• Essential Maths for Machine Learning
  • Linear Algebra
    • Vector and Matrices, Dot product, Projections, System of Equations, Matrix Transformation, Eigen Vectors and Values, Orthonormal Basis Vectors, SVD, PCA
  • Coordinate Geometry
    • Line, Plane, HyperPlane, Half space, Classification using plane
  • Calculus
    • Functions, Limits, Derivatives, Partial derivatives, Saddle points
• Supervised Learning
  • Linear Regression, Gradient Descent, Multicollinearity, VIF, R-square, Heteroscedasticity, Sklearn, Polynomial Regression, Bias-Variance trade-off, Regularisation
  • Logistic Regression, Squashing function, AUC. ROC, Precision-Recall Curve, Confusion matrix, Specificity
  • KNN, Decision Trees, Ensemble learning, Bagging, Boosting, SHAP Values
  • Support Vector Machine
  • Bayesian Machine Learning
• Unsupervised Learning
  • KMeans, Customer Segmentation, Hierarchical, DBSCAN, Anomaly Detection, Local Outlier Factor, Isolation Forest, Dimensionality Reduction, PCA, t-SNE, GMM, Information Theory, Expectation Maximisation
• Recommender Systems
  • Collaborative/Content filtering, Propensity analysis, Cold start problem
• Predictive Modeling & Time Series Forecasting
  • EDA, Resampling, Autocorrelation, Forecasting, Seasonal Naive, Double/Triple Exponential (Holt) Residual Analysis
  • Stationarity tests, Autoregressive methods, moving average, ARIMA, SARIMA.
• Reinforcement Learning and Forecasting
  • Reinforcement Learning, Q-learning, Autonomous players, RNNs and LSTMs for forecasting.

• Neural Networks
  • Neural Networks – MLP, Backpropagation, Hyperparameter Tuning, Practical Aspects of DL
  • Keras, Tensorflow, Pytorch
• Computer Vision
  • Convolutional Neural Nets, Data Augmentation, Transfer Learning, CNN Visualisation
  • Popular CNN Architecture – Alex, VGG, ResNet, Inception, DenseNet, EfficientNet, MobileNet
  • Object Segmentation, Localisation and Detection
  • Generative Models, VAEs, GANs, Attention Models, Siamese Networks, Advanced CV.
• Natural Language Processing
  • Text Processing and representation – Tokenization, Stemming, Lemmatization, Vector space modeling, Cosine Similarity, Euclidean Distance
  • POS tagging, Dependency parsing, Topic Modeling, Language Modeling Embeddings
  • Recurrent Neural Nets, Information Extraction, Entity Recognition, Transformers, HuggingFace, BERT, Building Chatbots

• Project scoping, Experiment tracking using MLFlow/W&B, Scripting (Flask/FastAPI/Streamlit), Testing, Versioning, Docker, CI/CD pipelines, AWS lambda, Monitoring using AWS Kibana, Drift

Introduction

• What is a Version Control System (VCS)?

• Distributed vs Non-distributed VCS

• What is Git and where did it come from?

• Alternatives to Git

• Cloud-based solutions (Github, Gitlab, BitBucket etc)

Installation and Configuration

• Obtaining Git

• Installing Git

• Common configuration options

• GUI tools

Key Terminology

• Clone

• Working Tree

• Checkout

• Staging area

• Add

• Commit

• Push

• Pull

• Stash

Git – Local Repository Actions

• Creating a repository (git init)

• Checking status (git status)

• Adding files to a repository (git add)

• Committing files (git commit)

• Removing staged files (git reset)

• Removing committed files (git rm)

• Checking logs (git log)

Git – Remote Repository Actions

• Creating a remote repository (git init)

• Cloning repositories (git clone)

• Updating the remote repository from the local (git push)

• Updating the local repository from the remote (git pull)

Tagging in Git

• What are Git Tags?

• Listing tags

• Lightweight tags

• Displaying tag details (tag show)

• Annotated tags

• Checking out tags

• Pushing tags

• Pulling tags

Branching in Git

• What is a branch

• A note about andlt;HEADandgt;

• Listing branches

• Create new branch

• Checkout branch

• Pushing branches

• Pulling branches

Merging in Git

• Fetching Changes (git fetch)

• Rebasing (git rebase)

• Git Pull

Git Workflows

• Different ways of using Git

• Centralised

• Feature Branch

• Gitflow Workflow

• Forking Workflow

Git – Stashing Changes

• What is Stashing?

• Using Stash

• Creating a branch from a Stash

Advanced Repository Actions

• Removing untracked files (git clean)

• Remove staged changes (git reset)

• Revert a commit (git revert)

• Checkout a previous commit (git checkout)

Advanced Branching and Merging

• Deleting a Branch

• Fast forward merge

• Three way merge

• Resolving merge conflicts

• Cherry-Picking (git cherry-pick)

Advanced Git Configuration

• Aliases

• Submodules

• Patches

• Hooks

• Short Stories / Case Studies based on real industry experience and research – The correct methods for improvement and dealing with difficult situations is essential to mastering agile scrum. My experience in industry and research into the topic has been used to give you a solid grounding in the most concise way possible.

• Expert Knowledge – I give you a complete overview of how I dealt with difficult situations on the job and techniques used in the business work place without having to do a face to face course saving you hundreds if not thousands of dollars.

• Concise overview of agile scrum – Including the way that the scrum framework is used to deliver projects in industry.

1. How Data Science and Solve Many Common Business Problems

2. The Modern Tools of a Data Scientist – Python, Pandas, Scikit-learn, Seaborn, Matplotlib & Plotly (Manipulate Data and Create Information Captivating Visualizations and Plots).

3. Statistics for Data Science in Detail – Sampling, Distributions, Normal Distribution, Descriptive Statistics, Correlation and Covariance, Probability Significance Testing and Hypothesis Testing.

4. Machine Learning Theory – Linear Regressions, Logistic Regressions, Decision Trees, Random Forests, KNN, SVMs, Model Assessment, Outlier Detection, ROC & AUC and Regularization

5. Deep Learning Theory and Tools – TensorFlow 2.0 and Keras (Neural Nets, CNNs, RNNs & LSTMs)

6. Solving problems using Predictive Modeling, Classification, and Deep Learning

7. Data Science in Marketing – Modeling Engagement Rates and perform A/B Testing

8. Data Science in Retail – Customer Segmentation, Lifetime Value, and Customer/Product Analytics

9. Unsupervised Learning – K-Means Clustering, PCA, t-SNE, Agglomerative Hierarchical, Mean Shift, DBSCAN and E-M GMM Clustering

10. Recommendation Systems – Collaborative Filtering and Content-based filtering + Learn to use LiteFM

11. Natural Language Processing – Bag of Words, Lemmatizing/Stemming, TF-IDF Vectorizer, and Word2Vec

12. Big Data with PySpark – Challenges in Big Data, Hadoop, MapReduce, Spark, PySpark, RDD, Transformations, Actions, Lineage Graphs & Jobs, Data Cleaning and Manipulation, Machine Learning in PySpark (MLLib)

13. Deployment to the Cloud using AWS to build a Machine Learning API

Our fun and engaging 20 Case Studies include:

Six (6) Predictive Modeling & Classifiers Case Studies:

1. Figuring Out Which Employees May Quit (Retention Analysis)

2. Figuring Out Which Customers May Leave (Churn Analysis)

3. Who do we target for Donations?

4. Predicting Insurance Premiums

5. Predicting Airbnb Prices

6. Detecting Credit Card Fraud

Four (4) Data Science in Marketing Case Studies:

1. Analyzing Conversion Rates of Marketing Campaigns

2. Predicting Engagement – What drives ad performance?

3. A/B Testing (Optimizing Ads)

4. Who are Your Best Customers? & Customer Lifetime Values (CLV)

Four (4) Retail Data Science Case Studies:

1. Product Analytics (Exploratory Data Analysis Techniques

2. Clustering Customer Data from Travel Agency

3. Product Recommendation Systems – Ecommerce Store Items

4. Movie Recommendation System using LiteFM

Two (2) Time-Series Forecasting Case Studies:

1. Sales Forecasting for a Store

2. Stock Trading using Re-Enforcement Learning

Three (3) Natural Langauge Processing (NLP) Case Studies:

1. Summarizing Reviews

2. Detecting Sentiment in text

3. Spam Filters

One (1) PySpark Big  Data Case Studies:

1. News Headline Classification

Project-1: Pan Card Tempering Detector App -Deploy On Heroku

Project-2: Dog breed prediction Flask App

Project-3: Image Watermarking App -Deploy On Heroku

Project-4: Traffic sign classification

Project-5: Text Extraction From Images Application

Project-6: Plant Disease Prediction Streamlit App

Project-7: Vehicle Detection And Counting Flask App

Project-8: Create A Face Swapping Flask App

Project-9: Bird Species Prediction Flask App

Project-10: Intel Image Classification Flask App

Project-11: Sentiment Analysis Django App -Deploy On Heroku

Project-12: Attrition Rate Django Application

Project-13: Find Legendary Pokemon Django App -Deploy On Heroku

Project-14: Face Detection Streamlit App

Project-15: Cats Vs Dogs Classification Flask App

Project-16: Customer Revenue Prediction App -Deploy On Heroku

Project-17: Gender From Voice Prediction App -Deploy On Heroku

Project-18: Restaurant Recommendation System

Project-19: Happiness Ranking Django App -Deploy On Heroku

Project-20: Forest Fire Prediction Django App -Deploy On Heroku

Project-21: Build Car Prices Prediction App -Deploy On Heroku

Project-22: Build Affair Count Django App -Deploy On Heroku

Project-23: Build Shrooming Predictions App -Deploy On Heroku

Project-24: Google Play App Rating prediction With Deployment On Heroku

Project-25: Build Bank Customers Predictions Django App -Deploy On Heroku

Project-26: Build Artist Sculpture Cost Prediction Django App -Deploy On Heroku

Project-27: Build Medical Cost Predictions Django App -Deploy On Heroku

Project-28: Phishing Webpages Classification Django App -Deploy On Heroku

Project-29: Clothing Fit-Size predictions Django App -Deploy On Heroku

Project-30: Build Similarity In-Text Django App -Deploy On Heroku

Project-31: Heart Attack Risk Prediction Using Eval ML (Auto ML)

Project-32: Credit Card Fraud Detection Using Pycaret (Auto ML)

Project-33: Flight Fare Prediction Using Auto SK Learn (Auto ML)

Project-34: Petrol Price Forecasting Using Auto Keras

Project-35: Bank Customer Churn Prediction Using H2O Auto ML

Project-36: Air Quality Index Predictor Using TPOT With End-To-End Deployment (Auto ML)

Project-37: Rain Prediction Using ML models & PyCaret With Deployment (Auto ML)

Project-38: Pizza Price Prediction Using ML And EVALML(Auto ML)

Project-39: IPL Cricket Score Prediction Using TPOT (Auto ML)

Project-40: Predicting Bike Rentals Count Using ML And H2O Auto ML

Project-41: Concrete Compressive Strength Prediction Using Auto Keras (Auto ML)

Project-42: Bangalore House Price Prediction Using Auto SK Learn (Auto ML)

Project-43: Hospital Mortality Prediction Using PyCaret (Auto ML)

Project-44: Employee Evaluation For Promotion Using ML And Eval Auto ML

Project-45: Drinking Water Potability Prediction Using ML And H2O Auto ML

Project-46: Black Friday Sale Project

Project-47: Sentiment Analysis Project

Project-48: Parkinson’s Disease Prediction Project

Project-49: Fake News Classifier Project

Project-50: Toxic Comment Classifier Project

Project-51: Language Translator App Using IBM Cloud Service -Deploy On Heroku

Project-52: Predict Views On Advertisement Using IBM Watson -Deploy On Heroku

Project-53: Laptop Price Predictor -Deploy On Heroku

Project-54: WhatsApp Text Analyzer -Deploy On Heroku

Project-55: Course Recommendation System -Deploy On Heroku

Project-56: IPL Match Win Predictor -Deploy On Heroku

Project-57: Body Fat Estimator App -Deploy On Microsoft Azure

Project-58: Campus Placement Predictor App -Deploy On Microsoft Azure

Project-59: Car Acceptability Predictor -Deploy On Google Cloud

Project-60: Book Genre Classification App -Deploy On Amazon Web Services

Quizzes
Mock Interviews