Data Analyst Bootcamp

Course Outline:

Agile Scrum Methodology

• Scrum Introduction
• Scrum Team
• Scrum Artifacts
• Sprint Increment
• Spring planning
• Backlog
• Retrospective
• Project description and Case Study
• Practice exam and Knowledge check
• Certification (optional)

SQL

• SQL Fundamentals
• Group By
• Joins
• Advance SQL Commands
• Creating Databases and Tables
• Practice exam and Knowledge check
• Certification (optional)

Data Management Principles 

• Data management lifecycle and key roles in the data management world
• Data management organization and role expectations
• Introduction to core data management and concepts
• Data management maturity assessment overview
• Data Security and data handling overview
• Understand how data management relates to Big Data and Data Science
• Metadata management overview & application
• Reference data overview & application
• Data quality overview & application

Datawarehouse Fundamentals

• Data Warehouse Basics
• Data Warehouse Architecture
• Dimensional Modeling
• Understanding Facts and Dimensions
• ETL process and tools
• Case Study
• ETL vs ELT
• The Modern Data Warehouses

Linux and Shell Scripting Essentials

• File System and Access
• Linux Fundamentals
• System Administration Basics
• Shell Scripting overview
• Networking Services

Python Programming – Fundamentals

• Set Up
  • Set up development environment – Jupyter notebooks
  • Using python shell
  • Executing python script
  • Understanding python strings
  • Print statements in python
• Data Structures in python
  • Integers
  • Lists
  • Dictionaries
  • Tuple
  • File
  • Mutable and Immutable structures
• Selection and Looping Constructs
  • If/else/elif statements
  • Boolean type
  • “in” membership
  • For loop
  • While Loop
  • List and Dictionary Comprehension
• Functions
  • Defining functions
  • Variable scope – Local and Global
  • Arguments
  • Polymorphisms
• Modules
  • Creating modules
  • Importing Modules
  • Different types of imports
  • Dir and help
  • Examining some built-in modules
• Classes & Exceptions
  • Object Oriented Programming Introduction
  • Classes and Objects
  • Polymorphism – Function and Operator Overloading
  • Inheritance

Data Visualization Concepts

Descriptive Statistics Fundamentals

• Central Tendency
  • Mean
  • Median
  • Mode
• Spread of the Data
  • Variance
  • Standard Deviation
  • Range
• Relative Standing
  • Percentile
  • Quartile
  • Inter-quartile Range

Inferential Statistics Fundamentals

• Inferential Statistics
  • Normal Distribution
  • Central Limit Theorem
  • Standard Error
  • Confidence Intervals
  • Other Distributions
  • Samples
  • Hypothesis Testing
• Perform statistical analysis on a given data set.

Data Exploration and Preparation

• Data Exploration
  • Describe
  • Merging
  • Grouping
  • Evaluating Features
• Data Visualization
  • Line Chart
  • Scatterplot
  • Pairplot
  • Histogram
  • Density Plot
  • Bar Chart
  • Boxplot
  • Customizing Charts
• Perform Exploratory Data Analysis
• Data Cleaning
  • Dropping Rows
  • Imputing Missing Values
  • Feature Evaluating

Tableau or Power BI

Tableau Basics

• Data Analysis lifecycle and Tableau’s role in the data analysis world
• Introduction to Tableau tool
• Features and Functionalities of Tableau
• Connection to excel datasheet work on Union and Joins
• Connection to excel datasheet to create our first graph
• Perform Sorting, Filtering, Grouping of the data in the report
• Utilize the Marks card and Pages card in the visualization area
• Use Aggregate Measures / create trend lines / discuss the data plot in the report
• Create multiple graphs based on the data and requirement of the scenarios
• Create a dashboard with multiple graphs / reports included in
• Create our own story in a way to present to executives / senior managers
• Connect to additional data source and switch the session while doing hands on

Advance Tableau Desktop

• Connect to excel datasheet, walkthrough the trainer to create a report, use all the cards we familiarized during last session
• Introduction to table calculations in Tableau Desktop
• Perform basic arithmetic calculations, data math, logic statement, string manipulation using the data sets we are analyzing
• Introduction to Maps graph and its usage
• Plot the coordinate points on the map and analyze multiple views
• Correct the unrecognized co-ordinates and custom geocoding
• Connect to SQL database and plot graphs using single table data
• Connect to multiple data tables, use joins, plot graphs, analyze the data and create dashboards for presentation
• Solve a real-time business problem in the class and simulate the real time working environment by yourself
• R Integration

Beyond Tableau Desktop

• Learning about Tableau Public profile creation and its benefits
• Creation of Tableau Public profile in the class
• Generate basic visualization in your Tableau Public profile
• Tableau Server
  • Working with filters across tabs
  • Setting up subscriptions
  • Sharing and downloading visualizations and raw data
  • Working with the “pause” button
  • Creating visualizations in edit mode
  • Navigating between sites, projects, worksheets, and dashboards
  • Work with charts, graphs, maps, and pivots

Cloud Computing Foundations (AWS or GCP)

• Cloud Computing Overview
• Security with Google’s Cloud Infrastructure
• Understanding resource hierarchy
• IAM – Identity and Access Management
• Different IAM Roles
• Connecting to Google Cloud Platform

Big Data Overview

• History and background of Big Data and Hadoop
• 5 V’s of Big Data
• Secret Sauce of Big Data Hadoop 
• Big Data Distributions in Industry
• Big Data Ecosystem before Apache Spark
• Big Data Ecosystem after Apache Spark
• Comparison of MapReduce Vs Apache Spark
• Big Data Ecosystem after Apache Spark
• Understand Apache Architecture and Libraries like Streaming, Machine & Deep Learning, GraphX etc.

Data Science Essentials

• Overview of Data Science
• The Difference Between Business Analytics (BI), Data Analytics and Data Science
• The Field of Data Science
• The Data Science Process
  • Define the Problem
  • Get the Data
  • Explore the Data
  • Clean the Data
  • Model the Data
  • Communicate the Findings
• Identifying a problem and asking good questions

Machine Learning Overview

• History and Background of AI and ML
• Compare AI vs ML vs DL
• Describe Supervised and Unsupervised learning techniques and usages
• Machine Learning patterns
• Classification
• Clustering
• Regression
• Gartner Hype Cycle for Emerging Technologies
• Machine Learning offerings in Industry
• Discuss Machine Learning use cases in different domains
• Understand the Data Science process to apply to ML use cases
• Understand the relation between Data Analysis and Data Science
• Identify the different roles needed for successful ML project

Project & Use Case

• Project Overview
• Complete projects to get experience and practice
• Industry Use Case Studies

Certification

• Certification Overview
• Identify the right certification for you
• Tips to prepare for certification

Training material provided:

Yes (Digital format)