Data Analytics Course

Our Data Analytics Course is designed to provide practical learning and implementation of all the end-to-end lifecycles of a data analytics project. We train on the latest tools related to data analytics that match the latest data analyst job description. This course is intended to provide both communication and technical learning.

Working with a Data Server:

• Create a Data Server

• Login to your Data Server

• Create a Data Analytics environment on your Data Server

• Python3 3 and Jupyter notebook

• Bash

• Install Python libraries relevant to Data Analytics

• Install csvkit for Bash

Introduction to Bash

• Working with directories

• Using Bash to download a 7-million-row dataset

• Exploring raw data using Bash

• Options in Bash for wordcount

• Piping in Bash

• grep’ in Bash

• Variables in Bash

• Looping in Bash

• ‘while’ loop in Bash scripts

• ‘if’ statements

Descriptive Statistics

• Measures of Central Tendency

• Measures of Dispersion

• Measures of Relationship

Inferential Statistics

• 4 techniques of Population Sampling

• Types of data

• Six stages of the Analytics Methodology

• Introduction to Databases and SQL

SQL

• DDL statements

• DML statements

• Aggregation functions, GROUP BY

• CTE’s

• JOINs

• Single-Line Functions

• String functions

• Date and Time functions

• Numeric functions

• UNIONS

• MINUS

• INTERSECT

• CASE

• COALESCE

• Window functions

Introduction to Python:

• Datatypes in Python

• Loops and conditional statements

• User-defined functions

• Variables

• Built-in functions

• Objects in Python

• Mutable objects:

• List

• Dictionary

• Numpy arrays

• Immutable objects:

• Strings

• Tuples

• Operations on Numpy arrays

• Using Pandas

• Data Visualization using Python

Machine Learning (ML)

• Data Cleaning process

• EDA (Exploratory Data Analysis) process

• Exploring Time Series data

• 4 Methods for Time Series forecasting

• Adjustments and Transformations on Time Series data

• Predictive modeling methods:

• Overview of common ML algorithms

• Regression:

• Intro to Linear Regression

• Interpreting Regression results

• Building the best ML model

• Feature Engineering

• Dummy variables

• Classification approach:
  ? k-NN algorithm

PowerBI

Intro to PowerBI

• Get data spreadsheets

• Power Query Editor

• Joining tables

• DAX

• Adding new measures

• Format data

• Transform data

• Buttons, Themes,

• Filters, Slicers, and visualizations

• Employee Demographics dashboard
• Survey Data dashboard