How to Learn Data Analytics with AI: A Step-by-Step Guide

Introduction

With the development of Artificial Intelligence (AI) technology, learning Data Analytics has become much easier and exciting! Companies are now gathering lots of data from websites, social media, customer interactions and online transactions. However it’s important to have people in place with the right skills to help understand this data, spot patterns and help the business make better decisions.Data Analytics with AI is a great way to help! Using AI tools you’ll be able to help begin analysing data, automate repetitive tasks and create visuals to help spot key insights. If you’re looking to start a career in data analytics then combining traditional analytics skills with AI knowledge could be a great move.

Let’s look at some ways you can learn how to do Data Analytics with AI as a beginner!

1. Learn the basics of data analytics

First things first! It’s important to understand what Data Analytics actually involves. Data Analytics involves gathering, cleaning, ordering and analysing data to help spot key insights.For example if you run an online shopping website you might want to analyse your customer data to see what products are popular, what age groups are buying a lot and why some customers aren’t buying anymore.

There are 4 types of data analytics:

• Descriptive Analytics – Explaining what happened through reports and dashboards.
• Diagnostic Analytics – Helping to understand why something happened.
• Predictive Analytics – Using historical data to predict future trends.
• Prescriptive Analytics – Providing suggestions of what to do.

Understanding these analytics will give you a good foundation to start using more advanced AI analytics.

2. Learn Excel and basic data handling

One of the best tools to start with is Microsoft Excel. This will help you ORDER your data, calculate information and spot any patterns.Start by learning the basic functions such as SUM, AVERAGE, COUNT, IF, VLOOKUP/XLOOKUP. Once you’ve understood these, try learning how to sort, filter, use conditional formatting and create pivot tables.Using AI tools like Microsoft Copilot can help you learn Excel quicker. These can help you create formulas, summarise data and explain complex calculations.To get you started try using a simple sales dataset. Calculate your monthly revenue, see what products sell the most and see how they’re performing.

3. Learn SQL

SQL (Structured Query Language) is an important part of data analytics. Many companies will store data such as customer information, sales data and other important information in databases. SQL helps analysts extract this information.

Start by learning some basic SQL commands such as:

1. SELECT – To SELECT information.
2. WHERE – To filter information.
3. ORDER BY – To ORDER information.
4. GROUP BY – To GROUP data.
5. JOIN – To JOIN information from different tables.

Using an AI assistant can help you understand SQL queries and help you spot any errors. You can ask the AI tool to explain what it does in simple terms or create practice questions for you.Try not to rely on the AI tool too much though. Try creating your own queries to help build your problem solving skills.

4. AI Tools for data analytics

Now you know the basics of data handling, start learning about some AI Tools for data analytics. These will help to reduce the amount of manual work and make complex analytics easier.

Some helpful tools to start with are:

1. ChatGPT – Can help to explain data, create SQL queries, suggest formulas and help understand your analytics.
2. Microsoft Copilot – Can help with analysing data in Excel, creating formulas and summarising data.
3. Power BI – Can create interactive dashboards and visual reports. Has some AI analytics features.
4. Tableau – Can help with data visualisation and analytics to see trends.
5. Google Gemini – Can help with understanding your data sets, coming up with ideas and explaining analytics.

By learning these tools alongside traditional analytics you’ll feel more confident when working with real data sets.

5. Learn python for Advanced Data Analysis

Python is widely used for data analytics. It helps make data processing and analysis much more flexible. Whilst you don’t need to be an expert at coding as a beginner, learning some basic python will help improve your analytical skills.

Start by learning about variables, data types, conditions, loops and functions. Focus on learning some key libraries:

• Pandas – Cleaning and ordering data.
• NumPy – Performing numerical calculations.
• Matplotlib – Creating charts and graphs.
• Seaborn – Creating visually appealing statistical visuals.

Using AI tools can help explain your code, spot any errors and suggest improvements. Completing some small coding exercises will help you understand how Python can help with data analysis.

6. Practice with real data analytics projects

Whilst learning the theory and tools is important, practicing with projects will really help. As a beginner you could try:

• Sales Analysis – See what your monthly revenues are, what products are performing well and what customers are buying.
• Customer Behaviour Analysis – Understand what your customers like and what they’re buying.
• Social Media Analysis – See how much engagement you have, how many people you’re reaching, impressions and how your audience is growing.Student
• Performance Analysis – See how they’re performing academically and spot trends.Use AI to create example data sets, suggest questions to analyse and explain your findings. Try to complete each project on your own and write down the steps you took.

7. Create a portfolio to help with your career

Once you’ve completed a few projects create a professional portfolio. This will help employers understand how much practical knowledge you have.Include your excel reports, SQL queries, python projects and Power BI dashboards. Write a brief explaining what problem you were solving, what tools you used, what insights you found and what conclusions you came to.You should also work on improving your communication and problem solving skills. As a data analyst you’ll need to be able to explain your findings to management and the business.

Careers you could look into include:

• Junior Data Analyst
• Business Analyst
• Data Reporting Analyst
• Business Intelligence Analyst
• Data Visualisation Analyst

Conclusion

Learning Data Analytics with AI is a great way to help prepare you for the increasing demand for data driven professionals. Don’t try to learn everything straight away. Start with Excel, learn SQL, explore Python, practice with visualisation tools and gradually introduce AI.Remember that AI is a tool to help you. You’ll still need to understand how to solve business problems, understand your data and make meaningful decisions.By practicing consistently, working on real projects and having the right guidance you’ll gain the skills needed to explore some exciting opportunities.

If you want to gain industry relevant skills check out our Data Analytics training programmes at Login360.