According to Statista, the amount of data generated by companies and individuals

According to Statista, the amount of data generated by companies and individuals has massively increased over the past decade, and the global data generated and consumed is expected to reach 181 zettabytes by 2025. To make sense of this large volume of data, organizations need to gather and analyze it to prepare for the future – essentially, this is data analytics. In this video tutorial, you will learn all the necessary skills and knowledge to become a data analyst in 2022 within 12 hours. Our experienced trainers will cover the basics of data analytics, the top 10 data analyst skills for 2022, the top 10 data analysis tools, and various data analytics projects related to coronavirus, Spotify, world happiness report, and Olympics.
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What is Data Analytics?
Data analytics is the process of exploring and analyzing large data sets to find hidden patterns, unseen trends, discover correlations, and valuable insights to make business predictions. Data analytics improves the speed and efficiency of your business by eliminating guesswork and manual tasks from choosing the right content, planning marketing campaigns, and developing products. Today, businesses can identify insights on the fly for immediate decisions. To perform data analytics, modern tools and technologies are used.
Ways to Use Data Analytics
Improve decision-making
Better customer service
Efficient operations
Effective marketing
Steps Involved in Data Analytics Process
The data analytics process involves five steps:
Understand the problem
Data collection
Data cleaning
Data exploration and analysis
Interpret the results
Data Analytics Tools
The following are some of the seven data analytics tools that you can use to perform analytics:
Python
R Programming
Tableau
Power BI
Excel
SAS
Apache Spark
Data Analytics Tools
Data analytics has become an essential part of almost every sector of business. In this article, we will discuss some of the most popular data analytics tools:
Python: Python has a vast range of libraries for data analytics, including NumPy, Pandas, SciPy, Scikit-learn, PyTorch, and TensorFlow.
R: R is an open-source programming language used for numerical and statistical analysis. It has libraries such as ggplot, Tidevos, Plotly, Deployer, and Carrot.
Tableau: Tableau is a popular data visualization and analytics tool that helps you create a range of visualizations to interactively present the data, build reports, and dashboards to showcase insights and trends.
Power BI: Power BI is a business intelligence tool developed by Microsoft that supports multiple data sources with features that make data visually appealing. It supports features that help you ask questions to your data and get immediate insights.
ClickView: ClickView provides interactive analytics with in-memory storage technology to analyze vast volumes of data and use data discoveries to support decision-making.
Apache Spark: Apache Spark is an open-source data analytics engine to process data in real-time and carry out complex analytics using SQL queries and machine learning algorithms.
SAS: SAS is a statistical analysis software that can help you perform analytics, visualize your data, write SQL queries, perform statistical analysis, and build machine learning models to make future predictions.
Applications of Data Analytics
Data analytics has been used in almost every sector of business. Here are a few examples:
Retail: Retailers use data analytics to predict trends, recommend new products, and boost profitability.
Healthcare: Healthcare industries analyze patient data to provide lifesaving diagnosis and treatment options.
Manufacturing: Manufacturing sectors use data analytics to discover new cost-saving and revenue opportunities.
Banking: Banking and financial institutions use data analytics to make sound financial decisions, find out probable loan defaulters, customer churnout rate, and detect fraudulent transactions immediately.
Logistics: Logistics companies use data analytics to develop new business models that can ease their business and improve productivity.
Companies Using Data Analytics
Many companies use data analytics on a daily basis, including Amazon, Accenture, Cigna, Cerner, Target, McAfee, Rapido, Flipkart, and Walmart.
Types of Data Analytics
Data analytics can be broken down into three types:
Descriptive Analytics: This type of analytics studies the total units of furniture sold and the profit that was made in the past.
Predictive Analytics: Predictive analytics predicts the total units that would sell and the profit we can expect in the future.
Prescriptive Analytics: Prescriptive analytics combines descriptive and predictive analytics to find ways to improve sales and profit.
Top 10 Skills to Become a Data Analyst in 2022
If you’re looking for a career in data analytics, here are the top 10 skills that you need to possess:
Knowledge of programming languages such as Python and R.
Knowledge of SQL and databases.
Statistical analysis and modeling skills.
Data visualization skills using tools such as Tableau and Power BI.
Machine learning and deep learning skills.
Experience with big data technologies such as Hadoop, Spark, and NoSQL databases.
Business acumen and communication skills.
Problem-solving and critical-thinking skills.
Attention to detail.
Continuous learning and adaptability.
With these skills, you can make a successful career in data analytics and help companies make data-driven decisions.