Three Analytics Tools and Skills You Can Start Learning Right Now
By: Brenden Noblitt
Tags: Technology, Education, Data Analytics, Data, Data Science

Data Science / Data Analytics / Education

Three Analytics Tools and Skills You Can Start Learning Right Now

Whether you are in marketing analytics, data analytics, business intelligence, or data science, we as analysts usually have a handful of tools and skills that we all need to be great analysts. Whenever I first started learning analytics, I honestly had no clue where to start. There are so many different skills and tools available, that it can be a little overwhelming if you are trying to learn on your own.

After getting into the professional world and seeing the daily flow of analytics as well as talking with many different data professionals, I think I have a good idea as to a starting point for tools and skills. This is not a perfect list or the “right answer”, as every organization is a little different in terms of the skills and tools they use. Without further stalling, let’s get into it!

Disclaimer: I am not currently full time data professional, and will not be until May 2021. I am simply relaying my experiences from my summer internships, independent consulting, and personal practice. This is not meant to tell you what exactly you need to know, rather, this is to give you some practical advice from experiences I’ve had. For advanced technical information, please check out Towards Data Science. Any tools or products that are mentioned are not sponsored, they are simply my favorite product I am personally referring. If you are currently learning analytics on your own and need a good starting point, keep reading!

3 Analytical Tools to Help You Start Learning Analytics

There are so many different tools that analysts use every day. Most analysts have to be able to learn efficiently, as there are new tools being developed every month. That being said, there are a few tools that most analysts I have talked to use on a daily basis.

1. Structured Query Language (SQL)

Structured Query Language, or “SQL”, is probably the most common tool that analysts use. I personally use it in marketing analytics, and know that my data analytics and data science friends use it daily as well! While NoSQL and non-relational databases are on the rise, relational databases are still widely used and more common, so SQL is probably your best bet if you are just starting out learning analytics.

While SQL is known for being “old”, it is still very powerful and can do a lot of great things. Part of being an analyst or data scientist means being able to take care of your data and organize it for analytical use. Besides warehousing and storing data, SQL can do a lot of other great things:

Create ETL processes that feed into tools for statistical analysis, such as Python, R, or SAS

Created stored procedures to semi-automate basic maintenance tasks, such as updates

Do basic operational functions, such as multiplication or subtraction, which allows for aggregations

Filter large amounts of data to make analysis easier

While all of these applications are useful, at the end of the day, the true power of SQL comes from modifying the data to be used in analytics. If you have 100 rows of data to analyze, you could probably just clean it in Excel and push it to a tool for statistical analysis. On the other hand, if you have 700,000 rows of data, it is much more efficient to put that data in a database, and write a few queries that clean it, all in less than a minute. I would highly recommend learning SQL because not only does it give you control of your data, it allows you to think through data problems with a different perspective.

One of my favorite SQL courses that I took when I started learning data analytics was “SQL, by 365 Data Science”. I would recommend checking it out if you are starting to learn SQL!

2. Python / R

Now I know what you’re thinking, “Learning programming is hard, and I’ve tried so many times and just can’t get anything to stick”. I’d like to say a couple things on this topic. First, when you are learning Python or R for analytical purposes, the goal should not to be a full Python web developer or R developer in 3 months. When starting out, you are not learning how to be a developer, you are learning how to be an analyst. I say this because there are a lot of courses out there for Python and R that teach you way more than you need to know when you are starting out.

“When you first start learning analytics, the goal should not to be a full Python web developer or R developer in 3 months. When starting out, you are not learning how to be a developer, you are learning how to be an analyst.”

My advice for learning these is to spend 40% of your time learning the syntax and 60% of your time in projects applying it. If you just read a Python book or do the course, you won’t know Python from a practical standpoint, and it will be difficult to implement Python on real projects. The goal is not to memorize the syntax and be able to write a full application in your head. The goal should be to understand how to problem solve using Python or R. Now all that being said, here are some great applications for Python or R:

Run basic statistical analysis with minimal code (averages, standard deviations, etc.)

Filter and clean large data sets (deleting duplicates, fixing misspellings, etc.)

Develop applications that automate basic analysis

Run different types of models

Split large files into smaller ones (More useful than you think)

Python / R comes up quite a bit on job postings as well as already having a large community of data professionals. There are other options like Julia, SAS, and Go, but I can safely say Python and R have the largest communities and highest demand in the industry.

My personal training is in Python, and before you find a course if you want to learn Python, I would recommend checking out W3Schools: Python. This gives you a good overview of Python basics and any training material you use will make more sense once you get the basics down!

3. Tableau (Or Any Data Visualization Tool)

Tableau is one of the most popular data visualization tools on the market right now. While it is pretty expensive, it is personally my visualization tool of choice. If you are a college student, you can get a 1 year subscription for free! If you are in a position where you cannot get a hold of a Tableau license, I would also look into Microsoft Power BI. The free version of Power BI works great, and while it is not nearly as powerful as Tableau, it is still a great tool to have in your toolbox. Here are some things Tableau can do:

Create interactive dashboards with a lot of different functionalities

Create calculated fields to do specific calculations and aggregations of metrics

Do quick table calculations that run more advanced calculations with one click (percent of total, percent of different, etc.)

Automatically update visualizations if connected to a database

Again, Tableau is my personal preference, but any data visualization tool works. If you haven’t noticed already, with these three tools, you can store/maintain data (SQL), transform/analyze data (Python / R), and visualize it for the end user (Tableau). This basic process, also called ETL (Extract, Transform, Load), is a process that many analysts use to get raw data analyzed and sent to the end user.

I personally learned Tableau at my university, but Tableau has their own training material that I think is really strong, which can be found here. I would definitely recommend checking it out!

3 Skills Every Analyst Should Have

While it’s great to have a set of tools you can use, the tool is only as good as the individual using said tools. There are a couple broad skill sets that you can practice, even while learning the above tools, that will help you as an analyst or data scientist!

1. Problem Solving

We all have some sort of problem solving skills and use them every day. Life is full of problems that need to be solved. In my opinion, the most difficult part of problem solving is avoiding “paralysis by analysis”. Paralysis by analysis is when you get stuck trying to find the perfect solution to a problem. It is easy to fall into this “paralysis” because the more broad and complex a problem is, the more paths there are to a solution.

“In my opinion, the most difficult part of problem solving is avoiding “paralysis by analysis”. Paralysis by analysis is when you get stuck trying to find the perfect solution to a problem.”

This is the aspect of problem solving that most analysts are strong in because typically, analysts are given a broad problem that needs solving and receive little direction. It might be something like “We need a subscriber report in a month”, or “I keep getting an error message when I open Tableau, can you take a look at it?”. In order to train your brain to solve problems, I would suggest starting by doing small analytics projections and use this problem solving process:

Step 1: Identify your objective — What specifically is the problem you are trying to solve?

Step 2: Identify you decision variables — What are the things you can change and control?

Step 3: Identify your constraints — What are things you have to account for in order to reach a solution

If interested, I can write a separate article on this topic. No matter what skills you use, the ability to take a large problem and break it down into these 3 steps is invaluable. If you can do this, it doesn’t matter what tools you use, because you will be providing value to your organization or clients. You might have your own problem solving process, and by no means do you have to follow the process above. I personally have found that this process is effective and is used by many analysts and data scientists.

2. Adaptability

Being able to adapt is a very important skill for any analyst. Let me give you a scenario. You work hard and spend time learning how to use SQL, Python, and Tableau. You apply for a job as a data analyst at a big company and get an offer! You accept, and start your new position next week. You first day, you are so excited to put your new tools to work. You walk into your first strategy meeting, only to find that this week, the team is implementing a new tool, Alteryx. Now, you can take 1 of 2 approaches in this scenario. Either you will refuse to learn the new tool, stumble through your position because you don’t know how to use the tool, and probably end up quitting out of frustration. Second approach, you do some independent research and spend some time out of the office messing around with Alteryx, and find it isn’t that difficult to use, and are able to start using it the next week. Being flexible and adaptable is a huge skill to have because there are new analytical tools coming out all the time. Being able to learn these new tools efficiently and adapt to unusual situations will lead you down a path of success in the world of data.

3. Communication

When I talk to people about going into analytics, they always seem surprised when I tell them about how important communication is, even in analytics. Analytics and data science is a complex field, and as an analyst, your job is to make sense of what is complicated and discover new things. In my opinion, this thrill is what makes this field so interesting. The discoveries you make, however, obviously need to be shared with the end user. It takes solid communication skills to articulate complex ideas and make them digestible for everyone else who is not an analyst, or more frequently, people who are not skilled in understand numbers and technology. Whenever you send out reports and discoveries by email, or present them at meetings, you will get loads of questions, and sometimes they are not the most fun questions to answer. “I’m pretty sure my team did more sales than that, can you recheck your numbers?”, or “You said your “model” is projecting my team to have a decrease in sales this year, why is that?”. You will get many questions like these, and being able to articulate why a number is there will show your professionalism and skill as an analyst.

Conclusion

While this is not the perfect roadmap or everything you need to know, I hope this is a good starting point for anyone trying to get into analytics or data science! You will get frustrated and put in a lot of hours to learn these tools and skills, but I promise you, this field is such a rewarding field. High salaries, job security, and an intellectually stimulating job will all make it worth it! Be well, and stay safe!