Data Analysts Beginner Projects

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.

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.

Who this course is for:

• Beginner Data Analyst
• Beginner Data Engineers
• Beginner Data Scientist