Advance Data Analytics using SQL, Python, Statistics, Power BI and Tableau (Bootcamp)

Modules

• WEEK 1: Learn Python
  Introduction of Python

  Operators in Python

  Variables

  Data Types

  Conditions (if-else) (Python3- if, if-else, Nested-if)

  Loops

  Functions

  Data Structures (for Python)

  Exception Handling
• WEEK 2: STATISTICS
  Implementation of Mean, Variance, and Standard Deviation (in Python using NumPy)

  Derivative and Function minimization

  Probability Distribution

  Set I – Uniform Distribution

  Set II – Exponential Distribution

  Set III – Normal Distribution

  Confidence Interval

  Covariance and Correlation

  Random Variables (With Examples)

  Hypothesis Testing

  Basic Understanding

  T-testing

  Paired T-testing (detailed overview)

  P-value in ML

  F-Test

  Z-test

  Mathematical Explanation in ML

  Chi-Squared Test for Feature Selection

  ANOVA Test in Python

  ANOVA Test using one-way

  ANOVA using two-way
• WEEK 3: SQL
  Basics of relational databases

  Basic Queries: SELECT, WHERE LIKE, DISTINCT, BETWEEN, GROUP BY, ORDER BY

  Advanced Queries: CTE, Subqueries, Window Function

  Joins: Left, Right, Inner, Full

  Stored procedures and functions

  Assignment
• WEEK 4: MICROSOFT POWERBI
  Understanding Business Intelligence

  Understanding Power BI

  Getting Started with Power BI

  Getting Data in Power BI Desktop

  Data Transformation

  Introduction to Power Query (M Formula Language)

  Data Modelling

  Introduction to DAX

  Data Visualization

  Creating Custom Visuals

  Visual Interaction

  Exploring Filters

  Creating Custom Slicers

  Exploring Power BI Desktop Features

  Power Bi service Features

  Dashboard
• WEEK 5: COUNTINUATION OF POWERBI
• WEEK 6: TABLEAU
  Understanding Tableau

  Getting Started With Tableau

  Getting Data in Tableau Desktop

  Data Visualization

  Creating Custom Visuals

  Visual Interaction

  Exploring Filters

  Dashboard
• WEEK 7: Data Pre-processing, Data Visualization, Exploratory Data Analysis
  Overview of Data Visualization

  Data Visualization with Python (includes use cases of libraries such as Matplotlib, Seaborn)

  EDA in Python – Set I (basic techniques to analyze the data)

  EDA in Python – Set II (basic visual techniques)

  EDA on Iris Dataset (explanation of EDA & techniques involved for data visualization)

  EDA using Seaborn – Titanic Dataset (explanation of EDA & techniques involved for data visualization)
• WEEK 8: CAPSTONE PROJECT

Applied Learning Project

All courses in the bootcamp contain multiple hands-on labs and assignments to help you gain practical experience and skills with a variety of data sets. Build your data science portfolio from the artifacts you produce throughout this program. Course-culminating projects include:

Extracting and graphing financial data with the Pandas data analysis Python library

Generating visualizations and conducting statistical tests to provide insight on housing trends using census data

Using SQL to query census, crime, and demographic data sets to identify causes that impact enrollment, safety, health, and environment ratings in schools

*Please Note: All projects and class assignments are based upon instructors’ discretion and the faculty can modify or change the projects anytime.

Program Experience

Who Should Attend?

This online program is designed for anyone who is interested in acquiring programming skills in Python & Machine Learning. No prior programming knowledge is required.

Certificate

Upon successful completion of the program, participants will receive a digital certificate of completion from American Institute for IT Professionals. This is a training program, and it is not eligible for academic credit.

Why Python?

Python is a good starting point for first-time coders. It uses simple, natural language syntax, almost like spoken English. It is powerful and it is versatile, favored by such diverse industry giants as Netflix, PayPal, NASA, Disney, and Dropbox. Python is used by 87% of data scientists.

User-Friendly Syntax: As an interpreted language, Python has simpler, more concise syntax than Java. Python’s simple, concise syntax makes it easy to write algorithms with just a few lines of code.

Open-Source Libraries: Pre-written code is readily available, with algorithms at your disposal, so you do not have to start every project from scratch. You can benefit from highly specific libraries – physics, web development, gaming, machine learning – by simply importing algorithms and applying them to your own data. It is plug and play at its best, with new functionalities being added all the time.

Community Exchanges: Python’s popularity means it has great community support, with almost 8 million Python developers across the world to help you debug or resolve a programming challenge.

Compatibility: Python is a cross-platform language and can be integrated easily with Windows and other platforms.

Adaptability: Almost every field is adopting Python and needs both generalists and specialists who know how to use it. Fields as varied as gaming, web development, healthcare, and fintech prefer Python over other programming languages, making it the must-learn language for STEM professionals and data scientists.

Why Machine Learning?

Machine learning algorithms are transforming systems, experiences, processes, and entire industries. It’s no wonder that business leaders see these data-driven technologies as fundamental for the future—and that practitioners fluent in both fields are in high demand.

At American Institute for IT Professionals, we are fascinated by their world-changing potential, and we’ve created the training program that will come Master’s degree level content, to help students understand the fundamentals of AI and machine learning and how to apply them to solve complex, real-world problems.

Why SQL?

Our SQL course content is the perfect place to start if you are new to the world of database programming or database administration. In addition to learning basic SQL commands and syntax – such as how to insert, update, and delete information from tables – users will be able to practice those same commands through our free online SQL interpreter. Enter SQL commands to receive immediate results and experiment to better understand how database concepts such as table drops and table selects work.

The SQL tutorial and online interpreter currently supports a subset of ANSI SQL. The basics of commands such as select, insert, update, delete, and drop will be covered in this gentle introduction to Structured Query Language (SQL).

If you are already familiar with the basics of SQL, you can still use this database course as a refresher and practice common SQL commands and statements.

Why Statistics?

Statistics is a core component of data analytics and machine learning. It helps you analyze and visualize data to find unseen patterns. If you are interested in machine learning and want to grow your career in it, then learning statistics along with programming should be the first step. In this article, you will learn all the concepts in statistics for machine learning.

Statistics is a branch of mathematics that deals with collecting, analyzing, interpreting, and visualizing empirical data. Descriptive statistics and inferential statistics are the two major areas of statistics. Descriptive statistics are for describing the properties of sample and population data (what has happened). Inferential statistics use those properties to test hypotheses, reach conclusions, and make predictions (what can you expect).