Data Analytics course curriculum provides extensive knowledge of Data Collection,

Extraction, Cleansing, Exploration, and Transformation. Alongside the Data Mining,

Data Integration is done with feature Engineering to build Prediction models for Data

Visualization and deploying the solution. You name the skill set and our trainers are

always there to handle the new generation tools with latest versions. As a part of the

Data Analytics training, the range of skills and tools that are emphasized in the course

include Statistical Analysis, Text Mining, Regression Modelling, Hypothesis Testing,

Predictive Analytics, Machine Learning, Deep Learning, Neural Networks, Natural

Language Processing, Predictive Modelling, R Studio, Tableau, Spark, Hadoop,

programming languages like R and Python.

Objectives:

In this Data Analytics classes in DASVM, you will learn about the following topics:

• Excel
• Data wrangling with SQL
• Presto (SQL Interface)
• Intro to Data Science and Statistics
• Business Problem Solving across different domains
• Optimization techniques
• Predictive Modelling
• Time- Series Forecasting
• Feature Engineering
• Machine Learning Techniques
• Business Case Studies
• Power BI

Course content

Excel

Introduction

• MS office Versions (similarities and differences)
• Interface (latest available version)
• Row and Columns
• Keyboard shortcuts for easy navigation
• Data Entry (Fill series)
• Find and Select
• Clear Options
• Ctrl+Enter
• Formatting options (Font, Alignment, Clipboard (copy, paste special))

Referencing, Named ranges, Uses, Arithmetic Functions

• Mathematical calculations with Cell referencing (Absolute, Relative, Mixed)
• Functions with Name Range
• Arithmetic functions (SUM, SUMIF, SUMIFS, COUNT, COUNTA, COUNTIFS, AVERAGE, AVERAGEIFS, MAX, MAXIFS, MIN, MINIFS)

Logical functions

• Logical functions: IF, AND, OR, NESTED IFS, NOT, IFERROR
• Usage of Mathematical and Logical functions nested together

Referring data from different tables: Various types of Lookup, Nested IF

• LOOKUP
• VLOOKUP
• NESTED VLOOKUP
• HLOOKUP
• INDEX
• INDEX WITH MATCH FUNCTION
• INDIRECT
• OFFSET

Advanced functions

• Combination of Arithmetic
• Logical
• Lookup functions
• Data Validation (with Dependent drop down)

Date and Text Functions

• Date Functions: DATE, DAY, MONTH, YEAR, YEARFRAC, DATEDIFF, EOMONTH
• Text Functions: TEXT, UPPER, LOWER, PROPER, LEFT, RIGHT, SEARCH, FIND, MID, TTC, Flash Fill

Data Handling: Data cleaning, Data type identification, Remove Duplicates, Formatting and Filtering

• Number Formatting (with shortcuts)
• CTRL+T (Converting into an Excel Table)
• Formatting Table
• Remove Duplicate
• SORT
• Advanced Sort
• FILTER
• Advanced Filter

Data Visualization: Conditional Formatting, Charts

• Conditional formatting (icon sets/Highlighted color sets/Data bars/custom formatting)
• Charts: Bar, Column, Lines, Scatter, Combo, Gantt, Waterfall, pie

Data Summarization: Pivot Report and Charts

• Pivot Reports: Insert, Interface, Cross table Reports; Filter, Pivot Charts,
• Slicers: Add, Connect to multiple reports and charts
• Calculated field, Calculated item

Data Summarization: Dashboard Creation, Tips and Tricks

• Dashboard: Types, Getting reports and charts together, Use of Slicers.
• Design and placement: Formatting of Tables, Charts, Sheets, Proper use of Colours and Shapes

Connecting to Data: Power Query, Pivot, Power Pivot within Excel

• Power Query: Interface, Tabs
• Connecting to data from other excel files, text files, other sources
• Data Cleaning
• Transforming
• Loading Data into Excel Query

Connecting to Data: Power Query, Pivot, Power Pivot within Excel

• Using Loaded queries
• Merge and Append
• Insert Power Pivot
• Similarities and Differences in Pivot and Power Pivot reporting
• Getting data from databases, workbooks, webpages

VBA and Macros

• View Tab
• Add Developer Tab
• Record Macro: Name, Storage
• Record Macro to Format table (Absolute Ref)
• Format table of any size (Relative ref)
• Play macro by button
• Shape
• As command (in new tab)
• Editing Macros
• VBA: Introduction to the basics of working with VBA for Excel: Subs, Ranges, Sheets
• Comparing values and conditions
• If statements and select cases
• Repeat processes with For loops and Do While or Do Until Loops
• Communicate with the end-user with message boxes and take user input with input boxes, User Form

SQL

SQL

• Introduction to SQL
• Database Normalization and Entity Relationship Model(self-paced)
• SQL Operators
• Working with SQL: Join, Tables, and Variables
• Deep Dive into SQL Functions
• Working with Subqueries
• SQL Views, Functions, and Stored Procedures
• Deep Dive into User-defined Functions
• SQL Optimization and Performance
• Advanced Topics
• Managing Database Concurrency
• Practice Session

Database Introduction

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

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

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

Fundamentals Of Python

Anaconda Installation, Introduction to python, Data types, Operators

• Variables
• Data types (integer, Boolean, Float, List, tuple, string)
• Operators in python

Data types Contd, Slicing the data, Inbuilt functions in python

• Dictionaries
• Sequence methods
• Concatenate
• Repetition
• Len
• Min & max functions
• Index position
• Addition and deletion of elements
• Reverse
• Sorting

Sets, Set Theory, Regular Expressions, Decision making statements

• Sets
• re module (find all, search, split, match)
• If & else if Getting input from user
• Identity Operators

Loops, Functions, Lambda functions, Modules

• For loop
• While loop
• Functions
• Lambda functions
• Math module
• Calendar module
• Date & time module

Pandas, NumPy, Matplotlib, Seaborn

• Data frame creation using different methods
• Using Pandas analysis on Universities
• Salary data sets
• Visualization using Matplotlib and Seaborn
• NumPy introduction

Tableau

Introduction to Tableau

• What is Tableau ?
• What is Data Visualization ?
• Tableau Products
• Tableau Desktop Variations
• Tableau File Extensions
• Data Types, Dimensions, Measures, Aggregation concept
• Tableau Desktop Installation
• Data Source Overview
• Live Vs Extract

Basic Charts & Formatting

• Overview of worksheet sections
• Shelves
• Bar Chart, Stacked Bar Chart
• Discrete & Continuous Line Charts
• Symbol Map & Filled Map
• Text Table, Highlight Table
• Formatting: Remove grid lines, hiding the axes, conversion of numbers to thousands, millions, Shading, Row divider, Column divider
• Marks Card

Filters

• What are Filters ?
• Types of Filters
• Extract, Data Source, Context, Dimension, Measure, Quick Filters
• Order of operation of filters
• Cascading
• Apply to Worksheets

Calculations

• Need for calculations
• Types: Basic, LOD’s, Table
• Examples of Basic Calculations: Aggregate functions, Logical functions, String functions, Tableau calculation functions, numerical functions, Date functions
• LOD’s: Examples
• Table Calculations: Examples

Data Combining Techniques

• What is Data Combining Techniques ?
• Types
• Joins, Relationships, Blending & Union

Custom Charts

• Dual Axis
• Combined Axis
• Donut Chart
• Lollipop Chart
• KPI Cards (Simple)
• KPI Cards (With Shape)

Groups, Bins, Hierarchies, Sets, Parameters

• What are Groups ? Purpose
• What are Bins ? Purpose
• What are Hierarchies ? Purpose
• What are Sets ? Purpose
• What are Parameters ? Purpose and examples

Analytics & Dashboard

• Reference Lines
• Trend Line
• Overview of Dashboard: Tiled Vs Floating
• All Objects overview, Layout overview
• Dashboard creation with formatting

Dashboard Actions & Tableau Public

• Actions: Filter, Highlight, URL, Sheet, Parameter, Set
• How to save the workbook to Tableau Public website ?

Power BI

Power BI Introduction and Installation

• Understanding Power BI Background
• Installation of Power BI and check list for perfect installation
• Formatting and Setting prerequisites
• Understanding the difference between Power BI desktop & Power Query

The Power BI user interface, including types of data sources and visualizations

• Getting familiar with the interface BI Query & Desktop
• Understanding type of Visualization
• Loading data from multiple sources
• Data type and the type of default chart on drag drop.
• Geo location Map integration

Sample dashboard with Animation Visual

• Financial sample data in Power BI
• Preparing sample dashboard as get started
• Map visual Types and usages in different variation
• Understanding scatter Plot chart with Play axis and the parameters

Power BI artificial intelligence Visual

• Understanding the use of AI in power BI
• AI analysis in power bi using chart
• Q&A chat bot and the use in real life
• Hirarchy tree

Power BI Visualization

• Understanding Column Chart
• Understanding Line Chart
• Implementation of Conditional formatting
• Implementation of Formatting techniques

Power Query Editor

• Loading data from folder
• Understanding Power Query in detail
• Promote header, Split to limiter, Add columns, append, merge queries etc

Modelling with Power BI

• Loading multiple data from different format
• Understanding modelling (How to create relationship)
• Connection type, Data cardinality, Filter direction
• Making dashboard using new loaded data

Power Query Editor Filter Data

• Power Query Custom Column & Conditional Column
• Manage Parameter
• Introduction to Filter and types of filter
• Trend analysis, Future forecast

Customize the data in Power BI

• Understanding Tool tip with information
• Use and understanding of Drill Down
• Visual interaction and customization of visual interaction
• Drill through function and usage
• Button triggers
• Bookmark and different use and implementation
• Navigation buttons

Dax Expressions

• Introduction to DAX
• Table Dax, Calculated column, DAX measure and difference
• Eg:- Calendar, Calendar auto, Summarize, Group by etc
• Calculated Column
• Related, Lookup value, switch, Dated if, Rank x, Date functions
• Dax Measure and Quick Measure
• Remove filters, Keep filters, All, All selected, Time Intelligence Functions, Rolling average, YoY, Running total

Custom Visual

• Custom visual and understanding the use of custom
• Loading custom visual, Pinning visual
• Loading to template for future use
• Publishing Power BI

Power BI Service

• Introduction to app.powerbi.com
• Schedule refresh
• Data flow and use power bi from online
• Download data as live in power point and more

Machine Learning

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 pre-processing, 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.

Unsupervised Learning

• K-means– The K-means an algorithm that can be used for clustering problems in an unsupervised learning approach
• Dimensionality reduction– Handling multi dimensional data and standardizing the features for easier computation
• Linear Discriminant Analysis– LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data
• Principal Component Analysis– PCA follows the same approach in handling multidimensional data

Performance Metrics

• Classification reports– To evaluate the model on various metrics like recall, precision, f-support, etc.
• Confusion matrix– To evaluate the true positive/negative, and false positive/negative outcomes in the model.
• r2, adjusted r2, mean squared error, etc.

Time Series Forecasting 

• Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting

Artificial Intelligence

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

Deep Learning Introduction

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

TensorFlow Introduction

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

Computer Vision Introduction

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

Natural Language Processing (NLP)

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

AI Ethical Issues & Concerns

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

Data Analysis Associate

Comparison & Correlation Analysis

• Data comparison Introduction
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Performing Comparison Analysis on Data
• Performing correlation Analysis on Data
• Hands-on case study 1: Comparison Analysis
• Hands-on case study 2 Correlation Analysis

Variance & Frequency Analysis

• Concept of Variability and Variance
• Data Preparation for Variance Analysis
• Business use cases for Variance and Frequency Analysis
• Performing Variance and Frequency Analysis
• Hands-on case study 1: Variance Analysis
• Hands-on case study 2: Frequency Analysis

Ranking Analysis

• Introduction to Ranking Analysis
• Data Preparation for Ranking Analysis
• Performing Ranking Analysis with Excel
• Insights for Ranking Analysis
• Hands-on Case Study: Ranking Analysis

Break Even Analysis

• Concept of Breakeven Analysis
• Make or Buy Decision with Break Even
• Preparing Data for Breakeven Analysis
• Hands-on Case Study: Procurement Decision with break even

Pareto (80/20 RULE) Analysis

• Pareto rule Introduction
• Preparation Data for Pareto Analysis
• Insights on Optimizing Operations with Pareto Analysis
• Performing Pareto Analysis on Data
• Hands-on case study: Pareto Analysis

Time Series and Trend Analysis

• Introduction to Time Series Data
• Preparing data for Time Series Analysis
• Types of Trends
• Trend Analysis of the Data with Excel
• Insights from Trend Analysis
• Hands-on Case Study: Trend Analysis

Data Analysis Business Reporting

• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements
• Presenting the reports
• Hands-on case study: Create Data Analysis Reports

Advanced Data Analytics

Data Analytics Foundation

• Business Analytics Overview
• Application of Business Analytics
• Visual Perspective
• Benefits of Business Analytics
• Challenges
• Classification of Business Analytics
• Data Sources
• Data Reliability and Validity
• Business Analytics Model

Optimization Models

• Prescriptive Analytics with Low Uncertainty
• Mathematical Modeling and Decision Modeling
• Break Even Analysis
• Product Pricing with Prescriptive Modeling
• Building an Optimization Model
• Case Study 1: WonderZon Network Optimization
• Assignment 1: KERC Inc, Optimum Manufacturing Quantity

Predictive Analytics with Regression

• Mathematics beyond Linear Regression
• Hands on: Regression Modeling in Excel
• Case Study 2: Sales Promotion Decision with Regression Analysis
• Assignment 2: Design Marketing Decision board for QuikMark Inc.

Decision Modelling

• Prescriptive Analytics with High Uncertainty
• Comparing Decisions in Uncertain Settings
• Decision Trees for Decision Modeling
• Case Study 3: Decision modeling of Internet Plans, Monte Carlo Simulation
• Case Study 4: Kickathlon Sports Retailer Supplier Decision Modeling

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

HDFS & 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

Pyspark Introduction

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

Spark SQL & Hadoop HIVE

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

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

Kafka and Spark

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

Business Statistics

Descriptive Statistics

• Data Types, Measure Of central tendency, Measures of Dispersion
• Graphical Techniques, Skewness & Kurtosis, Box Plot

Probability and Normal Distribution

• Random Variable, Probability, Probability Distribution, Normal Distribution, SND, Expected Value

Inferential Statistics

• Sampling Funnel, Sampling Variation, Central Limit Theorem, Confidence interval
• Introduction to Hypothesis Testing
• Hypothesis Testing (2 proportion test, 2 t sample t test)
• Anova and Chisquare

Data cleaning and Insights

• Data Cleaning (Invalid cells, Blanks, Outliers, Null values)
• Imputation Techniques (Mean and Median)
• Scatter Diagram
• Correlation Analysis

Business Problem Solving, Insights, and Storytelling

• Finance
• Marketing
• Retail
• Supply Chain

KNIME

Introduction to KNIME 

• Learn about the KNIME tool that can be quite efficient for data analytics, creating workflows, etc.

Working with data in KNIME 

• Learn about creating workflows, loading datasets in KNIME, etc.

Loops in KNIME 

• Learn about the loops in KNIME that enable efficient data transformation in KNIME
• Web scraping in KNIME
• Learn about techniques in KNIME that enable web scraping to collect data directly from the web.

Feature Selection, Hyperparameter optimization in KNIME 

• Learn about hyperparameter optimization, and feature selection in KNIME that will enable efficient machine learning models.

Data Modeling

Feature Selection 

• Feature selection techniques in Python that include recursive feature elimination, Recursive feature elimination using cross-validation, variance threshold, etc.

Feature Engineering 

• Feature engineering techniques that help in reducing the best features to use for data modeling.

Model Tuning 

• Optimization techniques like hyperparameter tuning to increases the efficiency of the machine learning models.
• Data Warehousing

Introduction to Data Warehouse

• Introducing Data Warehouse and Business Intelligence, understanding the difference between database and data warehouse, working with ETL tools, and SQL parsing.

Architecture of Data Warehouse 

• Understanding the Data Warehousing Architecture, system used for Reporting and Business Intelligence, understanding OLAP vs. OLTP, and introduction to Cubes.

Data Modeling Concepts 

• The various stages from Conceptual Model, Logical Model to Physical Schema, Understanding the Cubes, benefits of Cube, working with OLAP multidimensional Cube, creating Report using a Cube.

Data Normalization 

• Understanding the process of Data Normalization, rules of normalization for first, second, and third normal, BCNF, deploying Erwin for generating SQL scripts.

Dimension and Fact Table 

• The main components of Business Intelligence – Dimensions and Fact Tables, understanding the difference between Fact Tables & Dimensions, and understanding Slowly Changing Dimensions in Data Warehousing.

SQL Parsing, Cubes, and OLAP 

• SQL parsing, compilation and optimization, understanding types and scope of cubes, Data Warehousing Vs. Cubes, limitations of Cubes and evolution of in-memory analytics.

Fundamentals Of R

Introduction to R, Installation of RStudio, Data Types in R

• Data types (Numeric, Char, Logical, Complex, Vector, List, Matrix, Factor, Array, Data frame)
• Relational operators
• Logical operators

Decision making statements, Loops, Functions

• If
• If else
• For loop
• While loop
• Repeat
• Functions

Built in Functions in R, Joins, dplyr and gg plot 2

• Merging data frames
• Analyzing Iris Dataset using apply functions
• dplyr package (Filter, Sel, Arrange)
• Data visualization using ggplot2
• Scatterplot
• Histogram
• Boxplot

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