<strong>Headline: 4 free and effective tools for budding data analysts</strong>
Data analysis necessitates a lot of complex functions to be completed. Some of them are storing, manipulating or editing data, predictive analysis and building prediction models among others. Right tools and their correct usage are essential for performing these tasks with precision. However, beginners in the field of data science may find it tough to use the necessary tools. Here is our list of easy-to-use tools to make the lives of budding data scientists easier.
Microsoft Excel
Purpose: Storing, manipulating or editing data, basic analysis and creating graphs or charts.
Highly suitable for beginners, Microsoft Excel has a free version that helps in creation of graphs and charts and has a wide range of options to choose from. Other features available on Excel are colour customisation, selecting layouts from multiple options, and image compression. Excel is also popular for its effective data manipulation such as performing different functions through the help of available formulas. It comes in handy during basic analysis, especially while creating pivot table or adding slicers to filter any data.
Akkio
Purpose: Predictive analysis and building prediction models
It is an AI-powered tool that works with high efficiency. Users need to upload their dataset, select the variable that they want to predict and let Akkio do the rest. It will build a neural network around the variable inputted, and generate results within minutes. Akkio also guides its users about the statistical effectiveness of the model displayed. Therefore, the best part about Akkio is that it just doesn’t predict results, it tries to determine the accuracy level of the models, and even points out the false positives.
SQL
Purpose: Querying and manipulating big data
SQL is a popular tool for querying and data manipulation, especially when it comes to handling big chunks of data with utmost efficiency. In fact, what takes hours in other apps, takes seconds in SQL. This is because SQL allows the user to send and receive larger files with ease, and even store data in plain text files, that are smaller in size and saves valuable storage space. Above all, SQL is highly effective when it comes to joining multiple datasets together.
Python
Purpose: Machine learning and automation
Python is the leading open-source programming language in the world, that is especially popular with data analysts. It is perfectly suitable for beginners as it is free, fast, easy to understand and contains lots of libraries. Python easily helps in storing and manipulating data which are otherwise time consuming on other apps. It helps in analysing data during machine learning, automation as well as app deployment. Python is both rapid and flexible while handling big data and performing difficult calculations. It also helps in data exploration and clearance of data, both while saving a lot of time.