DATA SCIENCE COURSE IN AIZAWL

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity

MODULE 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES

• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions

MODULE 4: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions

MODULE 5: PYTHON NUMPY PACKAGE

• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations

MODULE 6: PYTHON PANDAS PACKAGE

• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis

MODULE 1: DATA SCIENCE ESSENTIALS

• Introduction to Data Science
• Data Science Terminologies
• Classifications of Analytics
• Data Science Project workflow

MODULE 2: DATA ENGINEERING FOUNDATION

• Introduction to Data Engineering
• Data engineering importance
• Ecosystems of data engineering tools
• Core concepts of data engineering

MODULE 3: PYTHON FOR DATA SCIENCE

• Introduction to Python
• Python Data Types, Operators
• Flow Control statements, Functions
• Structured vs Unstructured Data
• Python Numpy package introduction
• Array Data Structures in Numpy
• Array operations and methods
• Python Pandas package introduction
• Data Structures : Series and DataFrame
• Pandas DataFrame key methods

MODULE 4: VISUALIZATION WITH PYTHON

• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
• Advanced Python Data Visualizations

MODULE 5: R LANGUAGE ESSENTIALS

• R Installation and Setup
• R STUDIO – R Development Env
• R language basics and data structures
• R data structures , control statements

MODULE 6: STATISTICS

• Descriptive And Inferential statistics
• Types Of Data, Sampling types
• Measures of Central Tendencies
• Data Variability: Standard Deviation
• Z-Score, Outliers, Normal Distribution
• Central Limit Theorem
• Histogram, Normality Tests
• Skewness & Kurtosis
• Understanding Hypothesis Testing
• P-Value Method, Types Of Errors
• T Distribution, One Sample T-Test
• Independent And Relational T Tests
• Direct And Indirect Correlation
• Regression Theory

MODULE 7: MACHINE LEARNING INTRODUCTION

• Machine Learning Introduction
• ML core concepts
• Unsupervised and Supervised Learning
• Clustering with K-Means
• Regression and Classification Models.
• Regression Algorithm: Linear Regression
• ML Model Evaluation
• Classification Algorithm: Logistic Regression

MODULE 1: MACHINE LEARNING INTRODUCTION

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification, And Regression
• Supervised Vs Unsupervised

MODULE 2: ML ALGO: LINEAR REGRESSION

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python

MODULE 3: ML ALGO: LOGISTIC REGRESSION

• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python

MODULE 4: ML ALGO: KNN

• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python

MODULE 5: ML ALGO: K MEANS CLUSTERING

• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python

MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA)

• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python

MODULE 7: ML ALGO: DECISION TREE

• Random Forest Ensemble technique
• How it works: Bagging Theory
• Modeling and Evaluation in Python

MODULE 8: ML ALGO: NAÏVE BAYES

• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python

MODULE 9: GRADIENT BOOSTING, XGBOOST

• Introduction to Boosting and XGBoost
• How it works: weak learners' concept
• Modeling and Evaluation of in Python

MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python

MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN)

• Introduction to ANN
• How It Works: Back prop, Gradient Descent
• Modeling and Evaluation of ANN in Python

MODULE 12: ADVANCED ML CONCEPTS

• Adv Metrics (Roc_Auc, R2, Precision, Recall)
• K-Fold Cross-validation
• Grid And Randomized Search CV In Sklearn
• Imbalanced Data Set: Smote Technique
• Feature Selection Techniques

MODULE 1: TIME SERIES FORECASTING - ARIMA

• What is Time Series?
• Trend, Seasonality, cyclical and random
• Autoregressive Model (AR)
• Moving Average Model (MA)
• Stationarity of Time Series
• ARIMA Model
• Autocorrelation and AIC 

MODULE 2: FEATURE ENGINEERING

• Introduction to Features Engineering
• Transforming Predictors
• Feature Selection methods
• Backward elimination technique
• Feature importance from ML modeling

MODULE 3: SENTIMENT ANALYSIS

• Introduction to Sentiment Analysis
• Python packages: TextBlob, NLTK
• Case study: Twitter Live Sentiment Analysis

MODULE 4: REGULAR EXPRESSIONS WITH PYTHON

• Regex Introduction
• Regex codes
• Text extraction with Python Regex

MODULE 5: ML MODEL DEPLOYMENT WITH FLASK

• Introduction to Flask
• URL and App routing
• Flask application – ML Model Deployment

MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL

• MS Excel core Functions
• Pivot Table
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Goal Seek Analysis
• Data Table
• Solving Data Equation with EXCEL
• Monte Carlo Simulation with MS EXCEL

MODULE 7: AWS CLOUD FOR DATA SCIENCE

• Introduction of cloud
• Difference between GCC, Azure, AWS
• AWS Service ( EC2 and S3 service)
• AWS Service (AMI), AWS Service (RDS)
• AWS Service (IAM), AWS (Athena service)
• AWS (EMR), AWS, AWS (Redshift)
• ML Modeling with AWS Sage Maker

MODULE 8: AZURE FOR DATA SCIENCE

• Introduction to AZURE ML studio
• Data Pipeline and ML modeling with Azure

MODULE 1: GIT INTRODUCTION

• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture

MODULE 2: GIT REPOSITORY and GitHub

• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo

MODULE 3: COMMITS, PULL, FETCH AND PUSH

• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING, AND MERGING

• Organize code with branches
• Checkout branch
• Merge branches

MODULE 5: UNDOING CHANGES

• Editing Commits
• Commit command Amend flag
• Git reset and revert

MODULE 6: GIT WITH GITHUB AND BITBUCKET

• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
• Bitbucket Git account

MODULE 1: BIG DATA INTRODUCTION

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
• Hands-on Map Reduce task

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language

MODULE 5: MACHINE LEARNING WITH SPARK ML

• Introduction to MLlib Various ML algorithms supported by MLib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest

MODULE 6: KAFKA and Spark

• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations

MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION

• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Business Intelligence Vs Business Analytics
• Data Driven Decisions With Bi Tools
• The Crisp-Dm Methodology

MODULE 2: BI WITH TABLEAU: INTRODUCTION

• The Tableau Interface
• Tableau Workbook, Sheets And Dashboards
• Filter Shelf, Rows And Columns
• Dimensions And Measures
• Distributing And Publishing

MODULE 3: TABLEAU: CONNECTING TO DATA SOURCE

• Connecting To Data File , Database Servers
• Managing Fields
• Managing Extracts
• Saving And Publishing Data Sources
• Data Prep With Text And Excel Files
• Join Types With Union
• Cross-Database Joins
• Data Blending
• Connecting To Pdfs

MODULE 4: TABLEAU: BUSINESS INSIGHTS

• Getting Started With Visual Analytics
• Drill Down And Hierarchies
• Sorting & Grouping
• Creating And Working Sets
• Using The Filter Shelf
• Interactive Filters
• Parameters
• The Formatting Pane
• Trend Lines & Reference Lines
• Forecasting
• Clustering

MODULE 5: DASHBOARDS, STORIES AND PAGES

• Dashboards And Stories Introduction
• Building A Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Story Points
• Animation With Pages

MODULE 6: BI WITH POWER-BI

• Power BI basics
• Basics Visualizations
• Business Insights with Power BI

MODULE 1: DATABASE INTRODUCTION

• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)

MODULE 2: SQL BASICS

• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
• Comments
• import and export dataset

MODULE 3: DATA TYPES AND CONSTRAINTS

• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment

MODULE 4: DATABASES AND TABLES (MySQL)

• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table

MODULE 5: SQL JOINS

• Inner join
• Outer join
• Left join
• Right join
• Cross join
• Self join

MODULE 6: SQL COMMANDS AND CLAUSES

• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries

MODULE 7: DOCUMENT DB/NO-SQL DB

• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
• MongoDB data management

MODULE 1: ARTIFICIAL INTELLIGENCE OVERVIEW

• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence.
• Why Artificial Intelligence Now?
• Ai Terminologies
• Areas Of Artificial Intelligence
• Ai Vs Data Science Vs Machine Learning

MODULE 2: DEEP LEARNING INTRODUCTION

• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks

MODULE 3: TENSORFLOW FOUNDATION

• TensorFlow Installation and setup
• TensorFlow Structure and Modules
• Hands-On: ML modeling with TensorFlow

MODULE 4: COMPUTER VISION INTRODUCTION

• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network

MODULE 5: NATURAL LANGUAGE PROCESSING (NLP)

• NLP Introduction
• Bag of Words Models
• Word Embedding
• Language Modeling
• Hands-On: BERT Algorithm

MODULE 6: AI ETHICAL ISSUES AND CONCERNS

• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai: Ethics, Bias, And Trust

Data Science is a multidisciplinary field that involves extracting insights and knowledge from data using scientific methods, algorithms, and tools.

Acquiring knowledge in Data Science is important as it enables individuals and organizations to make informed decisions, uncover patterns and trends, solve complex problems, and gain a competitive advantage in various industries.

The skills required to pursue a career as a Data Scientist include proficiency in programming languages (such as Python or R), statistical analysis, machine learning, data visualization, problem-solving, and domain knowledge.

One can efficiently learn Data Science through a combination of self-study using online resources, taking online courses or bootcamps, participating in data science competitions, working on real-world projects, and seeking mentorship from experienced professionals.

Some common obstacles faced by professionals in the field of Data Science include dealing with large and complex datasets, ensuring data quality and integrity, addressing privacy and ethical concerns, staying updated with rapidly evolving technologies, and effectively communicating insights to non-technical stakeholders.

The cost of a Data Science course in Aizawl may vary depending on the institution and the program, but it generally ranges from INR40,000 to INR50,000 or more.

The eligibility criteria for enrolling in a Data Science course may vary depending on the institution but typically require a minimum educational qualification of a bachelor's degree in a relevant field such as computer science, mathematics, or statistics. Some courses may also have prerequisites in programming and mathematics.

The field of Data Science offers promising prospects and abundant opportunities, with a growing demand for skilled professionals in industries such as finance, healthcare, e-commerce, marketing, and technology. Data Scientists can work as consultants, analysts, researchers, or leaders driving data-driven decision-making processes.

Obtaining a certification in Data Science is beneficial as it provides validation of one's skills and knowledge, enhances credibility in the job market, improves career prospects, and helps in standing out among other candidates.

A career in data science is generally considered safe due to the high demand for data-driven decision-making and the growing need for professionals with data analysis and machine learning skills. However, it's important to continually update skills and stay adaptable to evolving technologies.