• Post category:StudyBullet-7
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Gain Real World Data Analyst Skills by building a data analytics portfolio

What you will learn

Perform data exploration and analysis with SQL

Write SQL Queries to explore and analyse data

Write SQL CTE queries to extract and query data

Write SQL temporary table queries to extract and query data

Perform data exploration & manipulation with Pandas & Python

Transform Data with Power BI

Create visualizations with Tableau

Scrape data from websites

Perform prediction analysis

Visualize Qualitative data

Visualize quantitative data

Present data using stories

Description

Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

Data analysts are in high demand across all sectors, such as finance, consulting, manufacturing, pharmaceuticals, government and education.

The ability to pay attention to detail, communicate well and be highly organised are essential skills for data analysts. They not only need to understand the data, but be able to provide insight and analysis through clear visual, written and verbal communication.

Some responsibilities of a data analyst includes:

  • Developing records management processes and policies
  • identify areas to increase efficiency and automation of processes
  • set up and maintain automated data processes
  • identify, evaluate and implement external services and tools to support data validation and cleansing
  • produce and track key performance indicators
  • develop and support reporting processes
  • monitor and audit data quality
  • liaise with internal and external clients to fully understand data content
  • gather, understand and document detailed business requirements using appropriate tools and techniques
  • design and carry out surveys and analyse survey data
  • manipulate, analyse and interpret complex data sets relating to the employer’s business
  • prepare reports for internal and external audiences using business analytics reporting tools
  • create data dashboards, graphs and visualisations
  • provide sector and competitor benchmarking
  • mine and analyse large datasets, draw valid inferences and present them successfully to management using a reporting tool

If you’re getting ready to launch a new career as a data analyst, chances are you’ve encountered an age-old dilemma. Job listings ask for experience, but how do you get experience if you’re looking for your first data analyst job?

This is where your portfolio comes in. The projects you include in your portfolio demonstrate your skills and experience—even if it’s not from a previous data analytics job—to hiring managers and interviewers. Populating your portfolio with the right projects can go a long way toward building confidence that you’re the right person for the job, even without previous work experience.

As an aspiring data analyst, you’ll want to demonstrate a few key skills in your portfolio. These data analytics project ideas reflect the tasks often fundamental to many data analyst roles.

The hands on projects covered in this course includes :

Web scraping

While you’ll find no shortage of excellent (and free) public data sets on the internet, you might want to show prospective employers that you’re able to find and scrape your own data as well. Plus, knowing how to scrape web data means you can find and use data sets that match your interests, regardless of whether or not they’ve already been compiled.


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We will use  Python and  tools like Beautiful Soup to crawl the web for interesting data.

Data cleaning

A significant part of your role as a data analyst is cleaning data to make it ready to analyze. Data cleaning (also called data scrubbing) is the process of removing incorrect and duplicate data, managing any holes in the data, and making sure the formatting of data is consistent.   We will perform  some practical data cleaning .

Exploratory data analysis (EDA)

Data analysis is all about answering questions with data. Exploratory data analysis, or EDA for short, helps you explore what questions to ask. This could be done separate from or in conjunction with data cleaning. Either way, you’ll want to accomplish the following during these early investigations.   We will undertake a data exploration project in this course.

Data Analysis & Transformation

We will  also perform some data analysis and transformation

Data Visualization

Data visualization is an important aspect of data analysis.   As a data analyst you should be able to present

data in a visual way that will help businesses make important decisions.

English
language

Content

Python Setup

Introduction
What is Python
Installing Python on Windows
Installing Python on Macs
Installing Python On Linux
Create a virtual environment on Windows
Activate a virtual environment on Windows
Create a virtual environment on Macs
Activate a virtual environment on Macs
Create a virtual environment on Linux
Activate a virtual environment on Linux
What is Jupyter Notebook
Install Jupyter notebook
Running Jupyter Notebook Server
Create a new notebook
Jupyter Notebook Dashboard
Text Editor
Install Visual Studio Code

Project : Prediction Analysis

Create a basic house value estimator
Using Scikit-learn
Loading dataset : Part 1
Loading dataset : Part 2
Prediction Analysis : part 1
Prediction Analysis : part 2

Project: Web Scraping

What is Web Scraping
What we will scrape
Create and activate a virtual environment
Install Beautiful soup
Build a web scraping script: Part 1
Build a web scraping script: Part 2
Prototyping the script: Part 1
Prototyping the script: Part 2
Prototyping the script: Part 3
Prototyping the script: Part 4
Prototyping the script: Part 5
Scraping data

Project : Data Analysis & Manipulation with Pandas

Using Kaggle Datasets
Tabular Data
Exploring Pandas DataFrame
Manipulating Pandas DataFrame
Cleaning Data
Qualitative data
Quantitative data

Project: Data Cleaning & Visualization with Tableau

What is Data Visualization
What is Tableau
What is Tableau Public Desktop
Tableau Online
Tableau Public Desktop Overview : Part 1
Tableau Public Desktop Overview : Part 2
Connecting to data source
Join related data sources
Join data sources with inconsistent fields
Data Cleaning
Reordering Visuaization
Change Summary
Split text into multiple columns
Presenting data using stories

SQL Server Environment Setup

What is SQL Server
What is SQL
Download SQL Server
Install SQL Server
SQL Server Editions
Install SSMS
Connect SSMS to SQL Server
Download Sample Database
Create database

Project : Data Exploration with SQL

Data Preparation
Importing Datasets into database
How many continents do we have data for
What is possibility of dying from COVID
What percentage of population is infected with COVID
What countries has highest COVID infection per population
What countries has the highest deaths from COVID
What continent has highest deaths from COVID
What are the global COVID cases and death
What number of people have been vaccinated against COVID
Analysing data with SQL CTE
Using temporary tables for data
Using Views for data

Power BI Setup

What is Power BI
What is Power BI Desktop
Install Power BI Desktop
Explore Power BI Interface

Project : Data Analysis & Transformation with Power BI

Importing Microsoft Access Database File
Change the Locale
Connect to data source
Power Query Editor and Queries
Creating and managing query groups
Renaming queries
Splitting Columns
Changing data types
Removing and reordering columns
Duplicating and adding columns
Creating conditional columns
Connecting to files in a folder
Appending queries
Merge queries
Query dependency view
Transform less structured data