Data analytics has been in the spotlight over the last few years. And the demand for data and analytics is still on the rise. Organizations are constantly exploring more and more with data to improve their business models and strategies.
By 2023, the big data industry will be worth an estimated $77 billion. By analyzing their 100 million subscribers
, Netflix was able to influence 80% of content viewed by subscribers due to accurate data insights.
(source – sigmacomputing.com)
Every report, forecasts, and their outcome are extracted from data. The process of analyzing raw data to uncover specific information is Data analytics. However, how much do we know about the data analytics tools?
Data Analytics Tools
Raw data needs to be analysed for business decision making, optimizing business performances, studying customer trends, and finally delivering better products and services. There are many tools out there to assist this decision-making process, and choosing the right tool is a challenge for data analysts.
Here’s a list of 5 popular tools used by data analysts:
1. Power BI
Power BI, also known as Microsoft Power BI (Business Intelligence) is a business analytics tool used to analyze data and share insights with your organization. Its convenience of pulling data together to process it into intelligible insights, often through compelling and easy-to-understand charts and graphs, is what makes it popular.
Some highlights:
- Built-in dashboards and reports for SaaS Solutions
- Real-time dashboard updates
- Power Query provides options related to wrangling and cleaning data
- Extensive database connectivity capabilities
- Feature dashboard visualization
- Data exploration using natural language queries.
- Secure connection to your data sources may it be in cloud or on-premises
2. Tableau
Tableau is a tool used a lot in the data analytics field for visualization and business intelligence. It simplifies raw data into an easily digestible format.
Use it to perform data analysis fast and create visualizations through dashboards and worksheets. Tableau’s data output is really easy to understand by professionals at every level, even the non-technical ones.
Best Features:
- Creates fast and easy visualization
- Good customer support
- Helps combine, shape, and clean data for analysis
- Able to handle a large amount of data
- Use scripting languages such as R and Python
3. Python
Python was originally designed as an Object-Oriented Programming language for software and web development and later enhanced for data science. One of the fastest-growing programming languages today, it is a powerful Data Analysis tool and has a great set of friendly libraries for any aspect of scientific computing. Python is a free, open-source software, and is easy to learn.
With Python’s data analysis library Pandas you can just do anything! You can perform advanced data manipulations and numeric analysis using data frames. Pandas support multiple file-formats; for example, you can import data from Excel spreadsheets to processing sets for time-series analysis.
Pandas is a powerful tool for data visualization, data masking, merging, indexing and grouping, data cleaning, and much more.
4. R Programming:
R is one of the most popular Data Analytics tools used for data mining, modeling, and heavy statistical computing. This is a free, open-source software which is not just used for analyzing data, but also to create software and applications that can perform statistical analysis seamlessly.
Additionally, R has a graphical interface, making it a viable choice for carrying out a wide range of analytical modeling, like time series analysis, linear/non-linear modeling, and data clustering, etc. Most statisticians use this tool because of its ready-to-publish nature with plots, graphs, equations, and formulae.
5. Microsoft Excel
Analyzing data has become an essential skill these days. MS Excel might be the most primitive tool used among Data Analysts, but it is still a go-to option of any beginner in the field. Even if you are an expert in all of the above tools, you might still need to use Excel. It is still the most basic and popular tool used in the industry.
Using pivot tables to filter complex data or draw insights from data as per client requirements, Excel has a range of functionalities. Its advanced analytics options help in modeling capabilities.
Summing up:
The world of Data has become much simplified because of the Data Analytics tools. The above-mentioned Data Analyst tools are just a tip of the iceberg. Sign up for our Data Analytics Masterklass to enter the world of Data Analytics effortlessly!
With AltUni’s Masterklass on Data Analytics with Power BI, you get to,
- Learn Data Visualization techniques and build data-driven business strategies
- Build charts, interactive dashboards, and story interfaces for visualization of data and trends
- Use DAX language to build and handle data models through the use of formulas & expressions
- Get hands-on experience of Data Analytics & Visualization in 2 Capstone Projects
- Become a certified Data Analyst!
- Become an AltUni alumnus to get access to exclusive job opportunities & huge discounts on all current and future programs
- Get access to AltUni Career Services like 1-on-1 mock interviews & profile building sessions by industry experts.