Top 7 Data Analytics tools & how they are used
Here’s an overview of what you’ll find in this article
- Choosing the right Data Analytics tools can be tough – but we have a solution
- What are Data Analytics tools and what are they used for?
- How to choose the right Data Analytics tools?
- An oldie but a goodie – Excel
- SQL – the rising star
- Python – the best gateway coding languages to learn
- Colab – crossover is key
- Jupyter – a highly popular open-source computational notebook
- Dataiku – for collaboration, Machine Learning and more
- Tableau – presenting and visualising findings in a way that everyone can understand
- How to start learning Data Analytics & using these tools?
Choosing can be tough
If you’re interested in a career in data analytics, or just want to level up your experience with some analytics skills, there are different aspects to consider.
The first step usually is formulating the philosophy and reasoning of “why” am I analysing data. What is it that you want to achieve and why do you need data for it?
Once you answered those questions, you’ll need to decide what tools you’d like to invest time into learning. That can become quite complex as there are hundreds of tools at a data analyst’s disposal including: spreadsheet applications, SQL Consoles, tools for Data Visualisation and Statistical Analysis, Business Intelligence software, and many more.
Not to worry, we spoke with our data analytics trainers to find out what the best tools are for beginners.
But first thing’s first…
What are Data Analytics tools and what are they used for?
Data analytics tools are software and applications that data analysts use to more easily retrieve, manage, manipulate, analyse and visualise data in order to extract meaningful insights and help companies make better data-driven decisions.
How to choose the right Data Analytics tools?
Data analysts typically have a wide array of responsibilities within an organisation. This includes, not only analysing data but also capturing it, cleaning it and translating it into insights or stories that everyone from C-level executives to individual team members can understand.
This means, depending on the specific field, company and job role, a data analyst may need:
- Spreadsheet applications to arrange and analyse data
- Business Intelligence tools to store and analyse larger amounts of company data
- Knowledge of programming languages to construct algorithms, and crawl, clean, and model data
- Data modelling tools to structure databases and design business systems
- Data visualisation tools and platforms to present findings, insights and recommendations to the company
- And more…
But, if you’re just getting started, there are a few universal tools you should master. This will also give you the background knowledge you need to learn how to use other tools once you score your dream job.
Excel – A spreadsheet application
Every aspiring data analyst should know how to use Excel to analyse and visualise data sets. Although it may sound less impressive than the other tools on this list, you’d be surprised how many functions this well-known spreadsheet application has that you may not know about.
Here are just a few of the most useful functions for data analysts:
- Easily group, filter and arrange data using pivot table and data split
- Clean data of duplicates using duplicate removal
- Build complex equations
- Visualise data using PivotChart or by creating interactive dashboards using slicers
- Use VBA to automate tasks, loop through a range of cells and more
One drawback to using Excel is that it doesn’t allow you to work with big data sets, making it much more suited to smaller data analysis processes. However, there are plugins which allow you to sidestep this.
In GrowthTribe’s 12-week data analytics course, we’ll be using both Excel and Google Sheets.
SQL (Structured Query Language) consoles
SQL, or Structured Query Language, is the standard programming language used in relational database management systems. It allows you to create, maintain, update and retrieve data from relational databases. Although this language was first created in the 70s, it’s still being used by the likes of Oracle, Microsoft SQL Server and Access, just to name a few.
Based on just a few short commands such as “Select”, “Insert”, “Update”, “Delete”, “Create”, and “Drop”, it’s one of the easiest programming languages to learn. The simplicity of it can help prepare you for more complex programming languages.
With SQL you can go beyond simple spreadsheets and manipulate and manage high volume data sets. As the building block of the data management systems we know today, it’s also easy to integrate it into different scripting languages and systems.
How SQL consoles are used
To use SQL, you need query or database consoles that are attached to data sources. Consoles act like a coding terminal where you can type and run SQL commands. Some of the most common consoles include MySQL, PostgreSQL, MS SQL, and Oracle.
In our data analytics course, we use PostgreSQL. While Excel may be a great tool for smaller data sets, SQL offers a much faster, easier way to manipulate big data. It’s also easier to share and work on together with team members because your data and analysis are kept separate, meaning no risk of data corruption or lost files.
As a data analyst, having some familiarity in key coding languages will help make your job faster and easier but also allow you to conduct more complex analysis processes.
Python is one of the most popular coding languages amongst data analysts because its coding and syntax are relatively easy to learn. It also integrates well with other coding languages including C/C++, Java, PHP and C#.
Best of all, Python’s extensive support libraries enable a wide range of functionality. Data analysts typically use Python to:
- Quickly scan through large amounts of data using the parallel processing power of libraries like Pandas and Numpy
- Scrape data from the internet using Python Scrapy or Beautiful Soup
- Visualise data by creating charts and graphs with Matplotlib, Plotly or Seaborn
- Apply machine learning without having to go through complex computations with SciKitLearn, TensorFlow or PyTorch
It also has great features for text mining, image processing, sentiment analysis, natural language processing and more.
All of this makes Python one of the best gateway coding languages to learn. During our 12-week Data Analytics course, we won’t be covering Python in great detail but will show you some of its capabilities and allow for T-shaping this skill.
Google Colab is an interesting hybrid between Jupyter notebook, the Cloud and Google Drive that allows you to: organise and clean data, run code, create visuals, import image datasets, train an image classifier, build predictive models and more.
And this crossover is perhaps what makes it such a great tool for data analysis. Some of the key benefits are:
- There’s absolutely no setup required
- It runs on Google’s cloud servers, meaning it provides GPU and TPU processing, without the need for downloads
- The fact that it’s hosted by Jupyter Notebook and uses Google Docs collaboration features means it’s easy to share and work on together with your team
- You can easily link it to your GitHub profile
- And… it’s free
Jupyter is an open-source computational notebook. Like Google Docs, each Jupyter Notebook you create is a live, online resource that’s easy to share and work on simultaneously. However, instead of simple text, it also combines live code, computational output, equations and multimedia resources in a single document.
That makes it great for data cleaning, numerical simulation, statistical modelling, machine learning and more.
There are many reasons why Jupyter is one of the most popular tools on this list but the top two are perhaps because:
- The notebooks are run on the cloud, rather than on your pc, providing significantly stronger computing power
- It’s all in the name. Jupyter speaks several different programming languages, including Julia, Python and R (the 3 languages where it got its name from)
Similar to Python, we’ll demonstrate Colab’s & Jupyter’s abilities without teaching it in-depth. Depending on your initial capability scan & personal goals, you might go into greater detail during your T-shaping modules.
Dataiku is a platform for self-service analytics and machine learning. It’s built for teams so it’s highly collaborative and facilitates document and knowledge sharing, change management and team activity monitoring.
Like many of the other tools, it allows data analysts to find, profile, combine, clean and visualise data and automate workflows.
But, perhaps the coolest feature is its automated machine learning capability. This allows data analysts to take advantage of this technology, without needing the technical skills. Here are a few of the things you can do with the platform, no coding necessary:
- Train algorithms
- Make predictions
- Identify clusters
- Extract useful information about features
Along with this, it also has smart data ingestion capabilities, the ability to conduct geospatial analyses and stream Twitter data.
While managing and analyzing data is important, perhaps the most important part of a data analyst’s job is being able to share the invaluable insights that drive a company’s decision-making power. That’s why tools for presenting and visualising findings in a way that everyone can understand are key.
Tableau is one of our personal favourites for this function. Here’s why:
- It has both desktop and online versions making it mobile and easy to access multiple data sources
- It automatically parses, categorises and correlates data into a comprehensive analytical report
- You can then use this data to create clean, visually appealing dashboards, reports and graphs
- Best of all, you don’t need any coding or technical skills to use it
Learn how to use all these Data Analytics tools and more
Our 12-week Data Analytics course gives students the chance to learn how to use all of these tools in a hands-on learning environment. Instead of simple instruction, we believe the best way to learn is by putting lessons into context with real-life data analyst projects.
Furthermore, depending on the results of each person’s personal assessment, we provide further guidance on some of the following tools:
- PowerBI and Azure connections
- Using MongoDB/Mongoose (NoSQL databases)
- API integration
- Kaggle Datasets
- Importing GeoData and mapping to existing data sets
- Possibility of Google Data Studio work
- Using Python for basic machine learning models (previous python experience required) – SciKitLearn and PyTorch
Concluding thoughts & next steps
There are so many great tools out there for data analysts that, simply knowing where to start can be overwhelming. But mastering these 7 tools will give you the building blocks you need to start your journey into the exciting world of data analytics and maybe even data science down the line.
To learn more about how our instructors use these tools to give students a practical deep dive into data analytics, check out our course page where you can also download the detailed course curriculum.