Data Analyst Bootcamp - IIT Alumni

Data Analyst Bootcamp - IIT Alumni

Top Rated Bootcamp from IIT Alumni with hands on Practical Projects

Bestseller
120.46 20.48

About This Course

Why should you choose this course?

This Bootcamp comprises of five courses and is the most comprehensive Data analytics Bootcamp for anyone who wants to start a career as a data scientist or just grow his/her data analysis skills, this bootcamp covers all the below courses.

  1. Excel Basics: A course on Excel will help students in learning Data manipulation, Data reporting and Data Visualization in a very user-friendly way
  2. Google Big Query –Cloud Database: A course on SQL and Google Big Query will help students in understanding data operations in large databases. Google BigQuery is a cloud-based big data analytics web service for processing very large data sets.
  3. Google Data Studio –Cloud Data Visualization: In this course, students will learn The basics and the best practices of data visualization. They will also learn how to use Google Data Studio to its full potential to become proficient at Data Visualization and cloud based reporting tasks.
  4. Pentaho –ETL Automation: In this section, students will learn ETL with Pentaho (Extract, Transform, Load) that helps in extracting data from different sources, transform the data, and then finally load it into a data warehouse system.
  5. Basics of Machine Learning: Students will learn the basics of statistics, basics of Machine Learning and a crash course on Python.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. Instructors of the course have been teaching Data Science and Machine Learning for over a decade. 

We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:

I had an awesome moment taking this course. It broaden my knowledge more on the power use of SQL as an analytical tools. Kudos to the instructor! - Sikiru

Very insightful, learning very nifty tricks and enough detail to make it stick in your mind. - Armand

Why should you choose this Bootcamp?

We believe in teaching by example. This course is no exception. Every Section’s primary focus is to teach you the concepts through how-to examples. Each section has the following components:

  • Theoretical concepts and use cases of different forecasting models
  • Step-by-step instructions on implement forecasting models in excel
  • Downloadable Excel file containing data and solutions used in each lecture
  • Class notes and assignments to revise and practice the concepts

The practical classes where we create the model for each of these strategies is something which differentiates this course from any other course available online.

Other Information

  • Certificate will provided in this course on Completion
  • Full lifetime access
  • Available on Mobile & Laptop

What Students Will Learn In Your Course?

Course 1: Excel Masterclass

  • Essential Excel Formulas (Mathematical, textual, logical)
  • Lookup Formulas (Vlookup, Hlookup, Index, Match)
  • Data Tools (Sorting, Filtering and Validating)
  • Formatting data and tables
  • Pivot Tables
  • Charts in Excel
  • Shortcuts to increase productivity
  • Analytics in Excel

Course 2: Google Big Query

  • Big Query and SQL basics and Installation
  • Case Studies and Examples
  • Fundamental Big Query and SQL statements 
  • Selection commands: Filtering and Sorting
  • Aggregate Commands
  • JOINS
  • Subqueries
  • Mathematical and Date Time functions 
  • Textual functions
  • Regular expressions

Course 3: Google Data Studio

  • Theoretical concepts for Data Studio
  • Charts to highlight numbers
  • Charts for comparing categories
  • Charting maps of a country, continent, or region
  • Charts to highlight trends
  • Highlighting contribution to total
  • Relationship between two or more variables
  • Aggregating on two dimensions: Pivot tables
  • Branding a Report
  • Sharing and Collaborating on Data Studio report
  • Google Big Query Connection
  • Charting Best Practices

Course 4: Pentaho ETL Automation

  • Pentaho Data Integration(PDI) Installation and Setup
  • Basic concepts for foundational understanding
  • Extracting tabular data
  • Extracting non-tabular data
  • Extracting from an SQL table
  • Merging Data Streams
  • Data Cleansing
  • Data Validation
  • Transformation and Analytics steps
  • Conceptual understanding for Loading Data
  • Loading the data into a Data Mart
  • PDI Jobs

Course 5: Machine Learning basics

  • Basics of Statistics
  • Setting up Python and Jupyter Notebook
  • Introduction to Machine Learning 
  • Data Preprocessing: Univariate Analysis
  • Data Preprocessing: Univariate Analysis
  • Linear Regression: OLS
  • Test-Train Split
  • Linear Regression: Model Performance evaluation
  • Linear Regression: Result Interpretation
  • Other Linear Models: Subset Selection
  • Other Linear Models: The Lasso
  • Other Linear Models: Ridge Regression

Are There Any Course Requirements Or Prerequisites?

  • You will need a PC with any version of Excel installed in it
  • Basic understanding of Excel operations like opening, closing and saving a file

Who Are Your Target Students?

  • Anyone keen to start a career in data science or take their data science career to the next level.

Course Content

  • 296 lectures
  • 33:50:34
  • Resource Files & Support Group Link
  • Introduction
    00:04:47
  • Mathematical Functions
    00:06:50
  • Textual Functions
    00:07:47
  • Logical Functions
    00:11:23
  • Date-time (Temporal) Functions
    00:07:07
  • Lookup Functions
    00:08:46
  • Data Tools
    00:19:07
  • Formatting data and tables
    00:18:00
  • Pivot Tables
    00:08:25
  • Charts-Part 1
    00:09:59
  • Charts-Part 2
    00:12:00
  • Named Ranges
    00:10:04
  • Indirect Functions
    00:05:38
  • Excel Shortcuts
    00:12:33
  • Analytics in Excel
    00:15:48
  • Macros
    00:10:04
  • Course Flow
    00:06:41
  • PostgreSQL and PGAdmin Installation
    00:09:30
  • Setting up BigQuery on Google Cloud Platform
    00:08:15
  • BigQuery Interface
    00:18:06
  • CREATE
    00:11:46
  • CREATE in BigQuery
    00:12:48
  • Exercise 1 Create DB and Table
    00:01:24
  • INSERT
    00:09:07
  • INSERT in BigQuery
    00:06:23
  • Import data from File
    00:04:59
  • Importing data from File using BigQuery Web User Interface
    00:11:12
  • File Upload in Google Big Query through Google cloud sdk
    00:11:07
  • Importing data from Google Drive
    00:10:11
  • Exercise 2 Inserting and Importing
    00:00:57
  • SELECT
    00:03:45
  • SELECT in BigQuery
    00:03:53
  • SELECT DISTINCT
    00:06:05
  • SELECT DISTINCT in BigQuery
    00:02:54
  • WHERE
    00:04:02
  • WHERE in BigQuery
    00:05:44
  • Logical Operators - AND OR NOT
    00:06:10
  • Logical Operators in BigQuery
    00:07:28
  • Exercise 3 SELECT WHERE
    00:01:16
  • UPDATE
    00:05:25
  • UPDATE in BigQuery
    00:03:04
  • DELETE
    00:04:11
  • DELETE in BigQuery
    00:03:13
  • ALTER
    00:17:21
  • ALTER in BigQuery
    00:03:19
  • Exercise 4 Updating Table
    00:01:03
  • Restore and Back-up
    00:07:38
  • Data Set creation in BigQuery
    00:11:10
  • Exercise 5 Restore and Back-up
    00:00:51
  • IN
    00:04:24
  • IN in BigQuery
    00:02:52
  • BETWEEN
    00:05:40
  • BETWEEN in BigQuery
    00:04:06
  • LIKE
    00:08:52
  • LIKE in BigQuery
    00:07:40
  • Exercise 6 In Like Between
    00:00:51
  • ORDER BY
    00:07:42
  • ORDER BY in BigQuery
    00:04:41
  • LIMIT
    00:03:38
  • LIMIT in BigQuery
    00:05:49
  • Exercise 7 Sorting
    00:00:47
  • AS
    00:03:33
  • AS in BigQuery
    00:02:24
  • COUNT
    00:05:13
  • COUNT in BigQuery
    00:04:47
  • SUM
    00:03:24
  • SUM in BigQuery
    00:01:58
  • AVERAGE
    00:02:53
  • AVERAGE in BigQuery
    00:03:09
  • MIN MAX
    00:04:17
  • MIN MAX in BigQuery
    00:02:53
  • Exercise 8 Aggregate functions
    00:01:19
  • GROUP BY
    00:11:52
  • GROUP BY in BigQuery
    00:05:36
  • HAVING
    00:05:04
  • HAVING in BigQuery
    00:04:44
  • Exercise 9 Group By
    00:01:13
  • CASE WHEN
    00:05:18
  • CASE WHEN in BigQuery
    00:06:12
  • Introduction ot Joins
    00:02:25
  • Creating Datasets for Joins in BigQuery
    00:03:39
  • Inner Join
    00:08:05
  • INNER JOIN in BigQuery
    00:07:40
  • Left Join
    00:07:32
  • LEFT JOIN in BigQuery
    00:04:06
  • Right Join
    00:06:27
  • RIGHT JOIN in BigQuery
    00:05:02
  • Full Outer Join
    00:04:59
  • FULL OUTER JOIN in BigQuery
    00:05:06
  • Cross Join
    00:04:21
  • CROSS JOIN in BigQuery
    00:04:23
  • Except
    00:03:00
  • EXCEPT in BigQuery
    00:04:45
  • Union
    00:03:13
  • UNION in BigQuery
    00:04:41
  • Exercise 10 Joins
    00:01:18
  • Subqueries
    00:14:35
  • Subqueries in BigQuery
    00:05:51
  • Exercise 11 Subqueries
    00:01:21
  • Views
    00:07:21
  • Views in BigQuery
    00:03:19
  • Index
    00:06:39
  • Index in BigQuery
    00:01:32
  • Exercise 12 Views
    00:00:47
  • LENGTH
    00:03:31
  • LENGTH in BigQuery
    00:03:10
  • UPPER LOWER
    00:02:10
  • Changing Case in BigQuery
    00:02:05
  • REPLACE
    00:04:13
  • REPLACE in BigQuery
    00:03:10
  • TRIM LTRIM RTRIM
    00:06:56
  • TRIM LTRIM RTRIM in BigQuery
    00:05:16
  • CONCATENATION
    00:02:56
  • CONCATENATION in BigQuery
    00:05:26
  • SUBSTRING 1
    00:06:01
  • SUBSTRING 2
    00:05:41
  • LIST AGGREGATION 1
    00:05:01
  • LIST AGGREGATION 2
    00:05:08
  • Exercise 13 String Functions
    00:02:27
  • CEIL FLOOR
    00:03:34
  • CEIL FLOOR in BigQuery
    00:05:47
  • RANDOM
    00:05:06
  • RANDOM in BigQuery
    00:05:37
  • SETSEED
    00:04:11
  • SETSEED in BigQuery
    00:00:22
  • ROUND
    00:02:27
  • POWER
    00:02:25
  • POWER in BigQuery
    00:01:57
  • Exercise 14 Mathematical Functions
    00:01:33
  • CURRENT DATE TIME
    00:04:31
  • CURRENT DATE TIME in BigQuery
    00:04:51
  • AGE
    00:03:50
  • AGE in BigQuery
    00:08:13
  • EXTRACT
    00:08:17
  • EXTRACT in BigQuery
    00:03:49
  • Exercise 15 Date-time functions
    00:01:23
  • PATTERN MATCHING BASICS
    00:07:44
  • ADVANCE PATTERN MATCHING (REGULAR EXPRESSIONS)
    00:15:21
  • PATTERN MATCHING in BigQuery
    00:07:56
  • Exercise 16 Pattern Matching
    00:01:42
  • Why Data Studio
    00:09:53
  • Data Studio Home Screen Dataset vs Data Source
    00:06:12
  • Structure of Input data Dimensions vs Metrics
    00:04:12
  • Opening Data Studio
    00:09:08
  • Adding a data source
    00:06:15
  • Managing added data source
    00:11:36
  • Data Table
    00:10:08
  • Styling tab for data table
    00:14:00
  • Scorecards
    00:07:51
  • Simple Bar and Column chart
    00:07:39
  • Stacked Column chart
    00:05:22
  • GeoMap
    00:03:23
  • Time Series
    00:08:08
  • Line Chart and Combo Chart
    00:04:50
  • Pie Chart and Donut Chart
    00:05:46
  • Stacked Area Charts
    00:07:28
  • Scatter Plots and Bubble charts
    00:10:27
  • Pivot tables for cross tabulation
    00:06:34
  • Bullet Chart
    00:04:11
  • TreeMaps
    00:04:47
  • Branding a Report Brand Logo and Company Details
    00:05:31
  • Brand colors for report branding
    00:04:39
  • Filter controls for viewers
    00:09:05
  • URL Embed to include external content
    00:05:08
  • Blending data from multiple tables
    00:11:55
  • Downloading report as PDF and Page Management
    00:03:17
  • Sharing report and Data Credentials
    00:10:05
  • Sharing report using a link
    00:02:46
  • Scheduling emails
    00:03:42
  • Embeding report on Website
    00:02:36
  • Connecting and Visualizing Data in Google BigQuery
    00:10:01
  • Highlighting chart message
    00:03:25
  • Eliminating Distractions from the Graph
    00:06:54
  • Avoiding clutter
    00:05:35
  • Avoiding the Spaghetti plot
    00:05:27
  • Setting up environment and installing PDI
    00:05:16
  • Opening Spoon - The Graphical UI
    00:07:19
  • The example problem statement
    00:06:46
  • Demonstration of a PDI transformation
    00:22:44
  • Demonstration of a PDI Job
    00:17:40
  • What is ETL
    00:03:30
  • Data Warehouse Ops Database and Data mart
    00:05:22
  • Inmon vs Kimball Architecture
    00:03:33
  • ETL vs ELT
    00:03:11
  • Data and the ETL process
    00:10:10
  • Manually entering data into PDI
    00:12:45
  • Inputting Data from a TXT (text) file
    00:13:15
  • Input from multiple CSV files at the same time
    00:15:41
  • Inputting Data from an Excel file
    00:07:00
  • Extracting Data from Zipped files
    00:07:54
  • Extracting from XML
    00:06:47
  • Extracting from JSON
    00:05:20
  • Plan for importing sales Data
    00:03:23
  • Installing and setting up PostgreSQL
    00:09:30
  • Creating Sales table in SQL
    00:09:14
  • Extracting from an SQL table
    00:05:40
  • Storing Data on AWS S3
    00:06:13
  • Reading data from AWS S3
    00:07:58
  • Concepts Merging Data Streams
    00:07:52
  • Sorted Merge Step - Merging customer data
    00:19:35
  • Merging product data
    00:11:17
  • Append data stream - merging sales data
    00:15:55
  • Introduction to Data Cleansing
    00:07:25
  • Value Mapper Step
    00:06:50
  • Replace in String Step
    00:05:35
  • Fuzzy Match concepts
    00:03:14
  • Fuzzy Match Step in PDI
    00:11:25
  • Fuzzy Match Algorithms
    00:09:21
  • Formula Step and changing data format
    00:09:38
  • Common Data Cleaning Steps
    00:03:31
  • Introduction to Data validation
    00:04:50
  • Data_validation 1 - String-to-Int and integer range validations
    00:11:27
  • Data validation 2 - Checking Reference Values using stream look-up
    00:08:23
  • Data validation 3 - Order date shipping date using calculator step
    00:06:00
  • Common Data Validation steps
    00:03:52
  • Correcting the errors and merging with main stream
    00:11:47
  • Writing the errors to the log
    00:03:34
  • Writing the errors to a separate file
    00:05:30
  • Concatenating Address Fields
    00:05:41
  • Data Aggregation using Group-by
    00:09:08
  • Normalization and Denormalization
    00:07:05
  • Number Range Step
    00:04:57
  • Introduction to PDI - SQL connection
    00:06:04
  • Reading and filtering data from DB into PDI
    00:05:01
  • Updating and Inserting data into DB from PDI
    00:08:10
  • Deleting data from SQL DB using PDI
    00:03:46
  • Facts and Dimensions tables
    00:03:29
  • Surrogate Keys in Dimension tables
    00:03:52
  • Type 1 2 Slowly Changing Dimensions
    00:03:17
  • Schemas
    00:03:32
  • Creating tables in DB
    00:07:37
  • Loading Customer Data using combination lookup update step
    00:13:48
  • Loading product data using dimension lookup step
    00:12:03
  • Loading sales data after database lookup steps
    00:09:57
  • Scripting Steps
    00:07:15
  • PDI Jobs vs Transformation
    00:02:49
  • Controlling the flow of execution
    00:05:57
  • Setting variables using set variables step
    00:07:55
  • File and Folder Management
    00:07:22
  • Sending Email Step
    00:07:54
  • Abort Job Step
    00:03:47
  • Running using command prompt and scheduling
    00:06:33
  • Metadata injection
    00:13:14
  • Regular Expressions for advanced String Matching
    00:15:21
  • Course contents
    00:07:04
  • Installing Python and Anaconda
    00:03:04
  • Opening Jupyter Notebook
    00:09:06
  • Introduction to Jupyter
    00:13:26
  • Arithmetic operators in Python Python Basics
    00:04:28
  • Strings in Python Python Basics
    00:19:07
  • Lists Tuples and Directories Python Basics
    00:18:41
  • Working with Numpy Library of Python
    00:11:54
  • Working with Pandas Library of Python
    00:09:15
  • Working with Seaborn Library of Python
    00:08:57
  • Types of Data
    00:04:04
  • Types of Statistics
    00:02:45
  • Describing data Graphically
    00:11:37
  • Measures of Centers
    00:07:05
  • Measures of Dispersion
    00:04:37
  • Introduction to Machine Learning
    00:16:03
  • Building a Machine Learning Model
    00:08:42
  • Gathering Business Knowledge
    00:03:26
  • Data Exploration
    00:03:19
  • The Dataset and the Data Dictionary
    00:07:31
  • Importing Data in Python
    00:06:03
  • Univariate analysis and EDD
    00:03:33
  • EDD in Python
    00:12:11
  • Outlier Treatment
    00:04:15
  • Outlier Treatment in Python
    00:14:18
  • Missing Value Imputation
    00:03:36
  • Missing Value Imputation in Python
    00:04:57
  • Seasonality in Data
    00:03:34
  • Bi-variate analysis and Variable transformation
    00:16:14
  • Variable transformation and deletion in Python
    00:09:21
  • Non-usable variables
    00:04:44
  • Dummy variable creation Handling qualitative data
    00:04:50
  • Dummy variable creation in Python
    00:05:45
  • Correlation Analysis
    00:10:05
  • Correlation Analysis in Python
    00:07:07
  • The Problem Statement
    00:01:25
  • Basic Equations and Ordinary Least Squares (OLS) method
    00:08:13
  • Assessing accuracy of predicted coefficients
    00:14:40
  • Assessing Model Accuracy RSE and R squared
    00:07:19
  • Simple Linear Regression in Python
    00:14:06
  • Multiple Linear Regression
    00:04:57
  • The F - statistic
    00:08:22
  • Interpreting results of Categorical variables
    00:05:04
  • Multiple Linear Regression in Python
    00:14:13
  • Test-train split
    00:09:32
  • Bias Variance trade-off
    00:06:01
  • Test train split in Python
    00:10:19
  • Linear models other than OLS
    00:04:18
  • Subset selection techniques
    00:11:34
  • Shrinkage methods Ridge and Lasso
    00:07:14
  • Ridge regression and Lasso in Python
    00:23:50
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Start-Tech Academy

Start-Tech Academy
  • 4.9 (18)
  • 15 Reviews
  • 18 Students
  • 3 Courses

Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. 
Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. 

Founded by Abhishek Bansal and Pukhraj Parikh. 

Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in  MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.

Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.