Data Analytics: A simple guide for beginners!

What is Data Analytics?

Data analytics is a trending field that mainly focuses on increasing productivity and business gain through diverse amounts of raw data. we use different techniques to process these raw data and produce meaningful insights. These insights are responsible for understanding current trends, making future forecasts, and successful decision-making through which business growth will be achieved.

Types of Data Analytics

Data analytics is broken down into four basic types.

Skills Required for a Data Analyst

As a data analyst, you should possess the following skills:

• Data fetching, analyzing, and querying
• Data blending, data cleaning, data discovery, and data visualization
• Basic statistics knowledge
• Reports and dashboards
• Expertise in the domain
• Solution-oriented

Data Analytics Tools

Now it’s time to discuss how we can acquire the skills mentioned above that a data analyst needs, Data analytics tools are the solution. Let us look into some tools we must learn to become a data analyst.

1. Microsoft Excel
2. Tableau
3. Python
4. R
5. Jupyter Notebook
6. Apache Spark
7. SAS
8. Microsoft Power BI

1. Microsoft Excel :

Microsoft Excel is a commercially available spreadsheet software. Data analysts can mainly use this software for data wrangling and reporting. it’s a powerful data analysis tool with many plug-ins, even though It is poor at handling big data sets.

2. Tableau :

Tableau is one of the best commercial data analysis tools available. Tableau helps you create a variety of data visualizations, insights, and trends using worksheets, dashboards, and stories. It has a user-friendly visual drag-and-drop interface which makes the software highly demanding.

3. Python:

Python is an object-oriented open-source programming language which easy to learn and widely used. It supports a range of libraries for data manipulation, data visualization, and data modeling.

4. R :

R is an open-source programming language majorly used for numerical and statistical analysis. It provides a range of libraries for data analysis and visualization.

5. Jupyter Notebook :

Jupyter Notebook is an open-source web application that allows you to create interactive documents. These combine live code, equations, visualizations, and narrative text.

6. Apache Spark :

Apache Spark is an open-source data processing framework that helps to process vast data sets quickly.

7. SAS :

SAS is a statistical analysis software that can help you perform analytics, visualize data, write SQL queries, perform statistical analysis, and build machine learning models to make future predictions.

8. Microsoft Power BI :

Power BI is an interactive data visualization software product developed by Microsoft. With Power BI documentation, you get expert information and answers to address your needs, no matter how you use Power BI.

Why Data Analytics

Why data analytics is high in demand is not a wonder because it can guarantee improved productivity and profit that every industry/business dreams of through better decision-making, better customer service, Efficient operations, effective marketing, and optimizing business performance using excellent data analytics tools.

Starting as a Data Analyst, you can also progress in your carrier as Analyst II, Senior Analyst, and then Data Scientist or even Chief Technology Officer.