Python, Data Science, Data Analysis, AI, Data Wrangling, SQL, Machine Learning, Prediction Algorithms, NLP, Data Visualization, Pyspark

Python 

• Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc. 
• Python Basics: Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling etc.
• Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.

Excel Fundamentals 

•  Reading the Data, Referencing in formulas , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering 
•  Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security 
•  IF conditions, loops, Debugging, etc.

Excel For Data Analytics 

•  Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.  

Data Visualization with Excel

•  Charts, Pie charts, Scatter and bubble charts
•  Bar charts, Column charts, Line charts, Maps
•  Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Classification Problems using Excel

•  Binary Classification Problems, Confusion Matrix, AUC and ROC curve 
•  Multiple Classification Problems  

Information Measure in Excel

•  Probability, Entropy, Dependence 
•  Mutual Information 

Regression Problems Using Excel

•  Standardization, Normalization, Probability Distributions 
•  Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
•  Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression

SQL Basics – 

•  Fundamentals of Structured Query Language
•  SQL Tables, Joins, Variables 

Advanced SQL –  

•  SQL Functions, Subqueries, Rules, Views
•  Nested Queries, string functions, pattern matching
•  Mathematical functions, Date-time functions, etc. 

Deep Dive into User Defined Functions

•  Types of UDFs, Inline table value, multi-statement table. 
•  Stored procedures, rank function, SQL ROLLUP, etc.

SQL Optimization and Performance

•  Record grouping, searching, sorting, etc. 
•  Clustered indexes, common table expressions.

Extract Transform Load

•  Web Scraping, Interacting with APIs

Data Handling with NumPy

•  NumPy Arrays, CRUD Operations, etc.
•  Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, and matrices.

Data Manipulation Using Pandas

•  Loading the data, dataframes, series, CRUD operations, splitting the data, etc.

Data Preprocessing

•  Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.
•  Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross validation techniques, train-test split, etc.

Data Visualization

•  Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib.
•  Regression plots, categorical plots, area plots, etc, with Python seaborn.

Descriptive Statistics – 

•  Measure of central tendency, measure of spread, five points summary, etc. 

Probability 

•  Probability Distributions, bayes theorem, central limit theorem. 

Inferential Statistics –  

•  Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Introduction to Machine learning 

•  Supervised, Unsupervised learning.
•  Introduction to scikit-learn, Keras, etc.

Regression 

•  Introduction classification problems, Identification of a regression problem, dependent and independent variables.
•  How to train the model in a regression problem.
•  How to evaluate the model for a regression problem.
•  How to optimize the efficiency of the regression model.

Classification 

•  Introduction to classification problems, Identification of a classification problem, dependent and independent variables.
•  How to train the model in a classification problem.
•  How to evaluate the model for a classification problem.
•  How to optimize the efficiency of the classification model.

Clustering 

•  Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
•  How to train the model in a clustering problem.
•  How to evaluate the model for a clustering problem.
•  How to optimize the efficiency of the clustering model.

Supervised Learning 

•  Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
•  Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
•  Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.
•  Random Forest – Creating random forest models for classification problems in a supervised learning approach.
•  Support Vector Machine – SVM or support vector machines for regression and classification problems.
•  Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding local minimum and maximum of a given function.
•  K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
•  Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.

Unsupervised Learning 

•  K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.

Artificial Intelligence Basics 

•  Introduction to keras API and tensorflow

Neural Networks

•  Neural networks
•  Multi-layered Neural Networks
•  Artificial Neural Networks 

Deep Learning 

•  Deep neural networks
•  Convolutional Neural Networks 
•  Recurrent Neural Networks
•  GPU in deep learning
•  Autoencoders, restricted boltzmann machine 

Power BI Basics

•  Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.
•  Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.

DAX 

•  Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Featur

Data Visualization with Analytics  

•  Slicers, filters, Drill Down Reports
•  Power BI Query, Q & A and Data Insights

The Data Science capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals: 

•  Extracting, loading and transforming data into usable format to gather insights. 
•  Data manipulation and handling to pre-process the data.
•  Feature engineering and scaling the data for various problem statements. 
•  Model selection and model building on various classification, regression problems using supervised/unsupervised machine learning algorithms.
•  Assessment and monitoring of the model created using the machine learning models.

Text Mining, Cleaning, and Pre-processing

•  Various Tokenizers, Tokenization, Frequency Distribution, Stemming, POS Tagging, Lemmatization, Bigrams, Trigrams & Ngrams, Lemmatization, Entity Recognition.

Text classification, NLTK, sentiment analysis, etc  

•  Overview of Machine Learning, Words, Term Frequency, Countvectorizer, Inverse Document Frequency, Text conversion, Confusion Matrix, Naive Bayes Classifier.

Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling

• Language Modeling, Sequence Tagging, Sequence Tasks, Predicting Sequence of Tags, Syntax Trees, Context-Free Grammars, Chunking, Automatic Paraphrasing of Texts, Chinking.

Introduction to PySpark

•  Apache Pyspark framework, RDDs, Stopgaps in existing computing methodologies