Advanced Data Science and AI Course

• Source code Vs bytecode Vs machine code, Compiler Vs Interpreter, C/C++, Java Vs Python.

• Different type of code editors in python, Introduction to Anaconda and IDEs

• Variable Vs Identifiers, Strings Operators Vs Operand, Procedure oriented Vs Modular programming.

• Measures of Central Tendency & dispersion, Inferential statistics and Sampling theory.

• Different types of Programming Language, What is Compiler?, What is an Interpreter?

• How a Python Program runs on our system?, Features of Python Memory Management in Python, Different Implementations of Python.

• Conditional Statement, Loop Statement.

• Linting, Formatting, Understanding Python code, Command Line Arguments, Python Operators

• Working with functions, Parameters vs Arguments, Namespace vs Scope, Function call vs Function referencing

• Introduction to Exception Handling, Type of Errors, Nestedtry-except block & Default except for block.

• Introduction to Modular Programming, Importing Modules and different import statement, Types of Modules.

• Use of File Handling?, Type of Files, File Operation, What is File Handling?, Why do we need File Handling?, Type of Files, File Operation

• Intro & use of Regular Expression?, Regex module & important methods,Regex pattern and it’s interpretation.

• Intro & use of numpy, What is an array?, Array Operations using Numpy, Numpy and Scipy, Numpy and Pandas

Numpy vs Pandas, Exporting Dataframe to CSV and Excel, EDA using Pandas

• Lines & markers, Figures, Watermark, Shapes, Polygons and arrows Color maps, Autocorrelation study

• Working with seaborn on titanic dataset, Introduction & installation, Controlling figure asthetics, Different plots in seaborn

• Plotly, Pygal, Geopltlib etc.

• Introduction to Probability Principles, Random Variables and Probability principles, Discrete Probability Distributions - Binomial, Poisson etc, Continuous Probability Distributions - Gaussian, Normal, etc, Joint and Conditional Probabilities, Bayes theorem and its applications, Central Limit Theorem and Applications

• Elements of Descriptive Statistics, Measures of Central tendency and Dispersion, Inferential Statistics fundamentals, Sampling theory and scales of measurement, Covariance and correlation

• Basic Concepts - Formulation of Hypothesis, Making a decision, Advanced Concepts - Choice of Test - t test vs z test, Evaluation of Test - P value and Critical Value approach, Confidence Intervals, Type 1 and 2 errors

• Ingest data, Data cleaning, Outlier detection and treatment, Missing value imputation, Capstone project for Business Analysis

• Types of Learning - Supervised, Unsupervised and Reinforcement, Statistics vs Machine Learning, Types of Analysis - Descriptive, Predictive, and Prescriptive, Bias Variance Tradeoff - Overfitting vs Underfitting

• Correlation vs Causation, Simple and Multiple linear regression, Linear regression with Polynomial features, What is linear in Linear Regression?, OLS Estimation and Gradient descent, Model Evaluation Metrics for regression problems - MAE, RMSE, MSE, and MAP

• Introduction to Classification problems, Logistic Regression for Binary problems, Maximum Likelihood estimation, Data Imbalance and redressal methodology, Up sampling, Down sampling and SMOTE

• Introduction to Unsupervised Learning, Hierarchical and Non-Hierarchical techniques, K Means Algorithms - Partition based model for clustering, Model Evaluation metrics – Clustering

• Introduction to KNNs, KNNs as a classifier, Non-Parametric algorithms and Lazy learning ideology, Applications in Missing value imputes and Balancing datasets

• Introduction to regularization, Understanding ridge regression, Working with Lasso regression, Tackling multicollinearitywith regression

• Nonlinear models for classification, Intro to decision trees, Why are they called Greedy Algorithms?, Information Theory - Measures of Impurity

• Introduction to Bagging as an Ensemble technique, Bootstrap Aggregation and Out of Bag error, Random Forests and its Applications in Feature selection, How Bagging overcomes the overfitting problem?, Scent and Boosting, How Boosting overcomes the Bias - Variance Tradeoff, Gradient Boosting and Xgboost as regularised boosting

• Intro to Time series, Autocorrelation and ACF/PACF plots, The Random Walk model and Stationarity of Time Series, Tests for Stationarity - ADF and Dickey- Fuller test, AR, MA, ARIMA, SARIMA models, A regression approach to time series forecasting.

Feature engineering & selection techniques, Principal Component Analysis, Linear Discriminant Analysis, Serving the model via Rest API & Keras.

Introduction to Neural Networks, Layered Neural networks, Activation Functions and their application, Back propagation and Gradient Descent

Introduction to TensorFlow, Working with TensorFlow, Linear regression with TensorFlow, Logistic regression with TensorFlow

Designing a deep neural network, Optimal choice of Loss Function, Tools for deep learning models - Tflearn and Pytorch, The problem of Exploding and Vanishing gradients

Architecture and design of a Convolutional network,Deep convolutional models & image augmentation

RN N & LSTM structure, Bidirectional RNNs and Applications on Sequential data, Advanced Time series forecasting using RNNs with LSTMs, LSTMs vs GRUs.

Intro to RBMs, Autoencoders, Application of RBMs in Collaborative filtering, Autoencoders for Anomaly detection, Capstone Project -Self-driving cars, Facial recognization.

Intro to the NLTK library, N-gram Language models: Perplexity and Smoothing, Introduction to Hidden Markov models, Viterbi algorithms, MEMMs and CRFs for named entity recognition, Neural Language models, Application of LSTMs to predict the next word.

Explicit and Implicit matrix factorization, Word2vec and Doc2vec models.

Introduction to Machine translation, Natural language processing, NLP with machine translation for text analysis, Word Alignment models, Encoder-Decoder Architecture, How to implement a conversational Chatbot

Fully functional chatbot, Front end backend and deployment process for chatbot

• What is RL? – High-level overview
• The multi-armed bandit problem and the explore-exploit dilemma
• Markov Decision Processes (MDPs)
• Dynamic Programming
• Monte Carlo Control
• Temporal Difference (TD) Learning (Q-Learning and SARSA)
• Approximation Methods (i.e., how to plug in a deep neural network or other differentiable model into your RL algorithm)

• Mathematics for Computer
• Vision Intro to Transfer Learning
• R-CNN and RetinaNet models for Object detection using Tensorflow
• FCN architecture for Image segmentation
• IoU and Dice score for model evaluation
• Face detection with OpenCV

• Array basics, Problem solving techniques with example
• Time Complexity & Bit manipulations
• Sorting, Searching & String Algorithms
• Linked list
• Two pointer techniques
• Stack & Queue - Implementation & Problems.
• Tree, Trie, Ternary Search tree
• Recursion & Greedy Algorithms
• Combinatorial problems with backtracking
• Hashing
• Graph Theory
• Dynamic Programming

Introduction to Excel interface, Customizing Excel Quick Access Toolbar, Structure of Excel Workbook, Excel Menus, Excel Toolbars: Hiding, Displaying, and Moving Toolbars, Switching Between Sheets in a Workbook, Inserting and Deleting Worksheets, Renaming and Moving Worksheets, Protecting a Workbook. Hiding and Unhiding Columns, Rows and Sheets, Splitting and Freezing a Window. Inserting Page Breaks, Advanced Printing Options, Opening, saving and closing, Excel document, Common Excel Shortcut Keys, Quiz.

Adjusting Page Margins and Orientation, Creating Headers, Footers, and Page Numbers, Adding Print Titles and Gridlines, Formatting Fonts & Values, Adjusting Row Height and Column Width, Changing Cell Alignment, Adding Borders, Applying Colours and Patterns, Using the Format Painter, Formatting Data as Currency Values, Formatting Percentages, Merging Cells, Rotating Text, Using Auto Fill, Moving and Copying Data in an Excel Worksheet, Inserting and Deleting Rows and Columns.

Inserting Excel Shapes, Formatting Excel Shapes, Inserting Images, Working with Excel SmartArt.

Entering and selecting values. Using numeric data in excel, Working with forms menu, cell references, conditional,Formatting and data validation, Finding and replacing information from worksheet, Inserting & deleting cells, rows and columns.

Creating basic formulae in excel, Implementing excel formulae in worksheet, Relative cell referencing, Absolute cell referencing, Relative vs Absol ute cell references in formulae, Understanding the order of operation, Entering and Editing text, Fixing errors in your formulae,Formulae with several operators, Formulae with cell ranges, Quiz.

Working with functions like SUM(), AVERAGE() etc, Adjacent cells error in excel calculations, Use of AutoSum & autofill command, Quiz

Creating a column chart, Working with the excel chart ribbon, Adding and modifying data on an Excel chart, Formatting an excel chart, Moving a chart to another worksheet, Resizing a chart, Changing a chart’s source data, Adding titles, gridlines and a data table, Formatting a data series and chart axis, Using fill effects, Changing a chart type and working with pie charts, Quiz.

Intro to Pivot Tables, Structuring Source Data for Analysis in Excel, Creating a PivotTable, Exploring Pivot Ta ble Analyse & Desig n Options, Working with and on pivot tables, Dealing with Growing Source Data, Enriching data with Pivot table calculated values & fields, Formatting and charting a PivotTable, Pivot Table Case Study, Quiz

Introduction to macros, Automating Tasks with Macros, Recording a Macro, Playing a Macro, Assigning a Macro a Shortcut Key.

What is a Database?, Why SQL?, All about SQL Difference between SQL & MongoDB, Different Structured Query languages Why MySQL?, Installation of MySQL, DDL, SQL Keywords, DCL, TCL, Database Vs Excel Sheets, Relational and database schema, Foreign and Primary Keys, Database manipulation, management, and administration.

Topics - What is HBase?, HBase Architecture, HBase Components, Storage Model of HBase, HBase vs RDBMS, Introduction to Mongo DB, CRUD, Advantages of MongoDB over RDBMS, Use cases, First Step in SQL Database, Creating Database, Dropping Database, Using Database, Introduction to Tables, Data types in SQL, Creating a table, Dropping table, Coding best practices in SQL.

Introduction to database, Creating Data base, Dropping Database, Using Database, Introduction to Tables, Data types in SQL, Use case of different data, Working with tables, Coding best practices in SQL

SELECT Statement, COUNT, SELECT WHERE, ORDER BY.

IN, NOT IN, NULL and NOT_NULL, Comparison Operators (=, >, >=, <=), MySQL Warnings (Understand and Debug).

SELECT DISTINCT, LIKE, NOT LIKE, ILIKE, LIMIT, BETWEEN, BETWEEN – AND

Multiple INSERT, INSERT INTO, GROUP BY HAVING, WHERE vs HAVING, UPDATE, DELETE, AS, EXISTS-NOT EXISTS, Aggregator functions, Application of group by, Count function, MIN and MAX, Sum Function, Avg Function.

Introduction to JOINs, Types of JOINS, Usage of different types of JOINS, Loading Data, Usage of string functions like; CONCAT, SUBSTRING etc, INNER join, OUTER join, Full join, Left Join, Right Join, UNION.

Local, Session, Global Variables, Timestamps and Extract, CURRENT DATE & TIME, EXTRACT, AGE, TO_CHAR, Mathematical Functions and Operators

CEIL & FLOOR, POWER, RANDOM, ROUND, SETSEED, Operators and their precedence.

Databases, Collection & Documents, Shell & MongoDB drivers, What is JSON Data, Create, Read, Update, Delete, Working with Arrays, Understanding Schemas and Relations.

What is MongoDB?, Characteristics, Structure and Features, MongoDB Ecosystem, Installation process, Connecting to MongoDB database, What are Object Ids in MongoDb, Data Formats in MongoDB, MongoDB Aggregation Framework, Aggregating Documents, What are MongoDB Drivers? Finding, Deleting, Updating, Inserting Elements.

What is TABLEAU?, Why to use TABLEAU?, Installation of TABLEAU, Connecting to data source, Navigating Tableau, Creating Calculated Fields, Adding Colours, Adding Labels and Formatting, Exporting Your Worksheet, Creating dashboard pages, Different charts on TABLEAU (Bar graphs, Line graphs, Scatter graphs, Crosstabs, Histogram, Heatmap, Tree maps, Bullet graphs, etc.), Dashboard Tricks, Hands on exercises.

Pre-attentive processing, Length and position, Reference Lines, Parameters, Tooltips, Data over time, Implementation, Advance table calculations, Creating multiple joins in Tableau, Relationships vs Joins, Calculated Fields vs Table calculations, Creating advanced table calculations, Saving a Quick table calculation, Writing your own Table calculations, Adding a second layer moving average, Trendlines for power-insights.

Getting started with visual analytics, Geospatial data, Mapping workspace, Map layers, Custom territories, Common mapping issues, Creating a map, working with hierarchies, Coordinate points, Plotting latitude and longitude, Custom geocoding, Polygon Maps, WMS and Background, Image Creating a Scatter Plot, Applying Filters to Multiple Worksheets.

Aggregation and its types, level of detail common calculation functions, creating parameters

Tiled vs Floating, Working in views with Dashboard and stories, Legends, Quick filters.

Why Power BI?, Account Types, Installing Power BI, Understanding the Power BI Desktop Workflow, Exploring the Interface of the Data Model, Understanding the Query Editor Interface.

Connecting Power BI Desktop to Source Files, Keeping & Removing Rows, Removing Empty Rows, Create calculate columns, Make first row as headers, Change Data type, Rearrange the columns, Remove duplicates,Unpivot columns and split columns, Working with filters, Appending queries, Working with columns, Replacing values, Splitting columns, Formatting data & handling formatting errors, Pivoting & unpivoting data, Query duplicates vs references

Power BI, Working with Time series Understanding aggregation and granularity

Filters and Slicers in Power BI, Maps, Scatterplots and BI Reports, Creating a Customer Seq mentation.

Understanding Relationships, Many-to-One & One-to-One, Cross Filter Direction & Many-to-Many, M-Language vs DAX (Data Analysis Expressions), Basics of DAX, DAX Data Types, DAX Operators and Syntax, Importing Data for DAX Learning, Resources for DAX Learning, M vs DAX, Understanding IF & RELATED, Create a Column, Rules to Create Measures, Calculated Columns vs Calculated Measures, Understanding CALCULATE & FILTER, Understanding Data Category, SUM, AVERAGE, MIN, MAX, SUMX, COUNT, DIVIDE, COUNT, COUNTROOMS, CALCULATE, FILTER, ALL, Time Intelligence, Create date table in M, Create date table in DAX, Display last refresh date, SAMEPERIODLASTYEAR, TOTALYTD, DATEADD, PREVIOUSMONTH.

Create data table in M and DAX, Display last refresh Date.

Create your first report, Modelling basics to advance, Modelling and relationship, Ways of creating relationship, Normalisation – De-normalisation, OLTP vs OLAP, Star schema vs Snowflake schema.