Data Analyst Track Breakdown

Importance of data in the industry

Today, data has become the hottest topic in technology. Why is that? Well, users of the internet around the world generate huge amounts of data and this data has become a company’s biggest asset. So, you may think “What do these companies even do with this data?” You see, companies can understand how they can optimize their product pages, their app design, or even social media ads by understanding their user activity. And they do this by hiring talented professionals to convert data into profitable business insights.

Now, I’m here to tell you that you can become one of those talented Data Analysts by learning the right skills which will help any such companies deliver valuable business decisions.

In this blog, you will learn the necessary skills required to become a Job-ready Data Analyst.

SQL – Oracle & Postgres

Now, if you are a complete beginner or have some idea about data, you need to start with databases. A Database is the one place where we store and handle all this data. And to interact with such databases you need to master the SQL language.

Why SQL? Well, any popular database that you take requires the language SQL to interact with this data. You need to learn SQL from scratch with one of the most popular databases like Oracle. You should also be preparing yourself with plenty of progressive real-world challenges which are available online. So, within just one month of study and practice, you are a job-ready Oracle SQL Master!

Next up, you are going to turn yourself into a SQL query wizard! What? Yes, you should start learning Postgres, another popular and widely database software in the open-source community. Why so? Postgres offers a wider variety of features compared to other databases. So, you need to get the opportunity to dive deep into Postgres by practicing puzzles available online with solutions that will make you extract critical insights from the databases. After learning Postgres Database, you’ll never be afraid to look at a large SQL query again.

Python + Data Visualization

Followed by this, you must start learning Python, one of the most in-demand programming languages in the world right now! Whether you want to work on web applications, machine learning, data science, automation and so much more. Python language is very easy to learn and requires no prerequisites, and it is perfect for anyone who is looking to land a job as an entry-level developer.

You can start by learning the fundamental programming concepts such as variables and datatypes then expand on to learn more comprehensive topics such as functions, control flow and master Object Oriented Programming thoroughly. Then finish off by covering classes, inheritance, python script files, file IO and more. At the end of each topic, make sure you give yourself the chance to practice everything you had covered with programming challenges with are available online. Then, whether you get these challenges right or wrong, check for in-depth solutions to understand WHY we solve things a certain way. So by doing all this you won’t just sit back and watch a bunch of videos, rather you get your hands dirty and practice writing code.

You may think there are tons of Python resources out there. But most of them expect you to learn by just watching or just reading. Well, that is not an effective way to learn! So, make sure you find the right resource that works for you.

Now, follow up with a level-up of your python skills by learning Data Analytics and Data Visualization which are critical and used in companies like Apple, Amazon, Facebook, and more with large amounts of raw, unorganized data. In the next few years, trillion gigabytes of data will need to be managed and processed by these companies and they are willing to pay top dollar to get skilled developers who can analyze and get insights from this data.

The Python libraries that you should be learning in this track can be broken down into two large categories- Data Analytics and Visualization. The Data Analytics side will include NumPy, SciPy, and Pandas. On the Visualization side, you should learn Matplotlib, Seaborn, and Ploty. Even I’m excited for you to list these libraries on your resume because you’ll see an instant jump in recruiters willing to interview you. These skills will get you to the point where you can use these libraries confidently and effectively in the workplace.

Not only that, mastering Data Analytics and Visualization skills will set you up to learn more advanced topics such as Machine Learning. So, skills to digest vast amounts of data and identify the important elements are very important. Once analyzed, data visualization is needed to tell a story and present the insights in a form where more people can understand the key points and make better decisions faster.

Tableau

By now, you are already a SQL wizard and python geek! So, what next?! Do you want to learn a super-hot industry skill that’s in demand more and more everywhere? I bet you do!

Let me introduce you to Tableau. With this technology, you will be able to present cool-looking visual dashboards and impress your teams with them. So if you want to learn a new technical skill, or if you are brand new to the world of data, then Tableau is the go-to technology to master!

In Tableau, you need to learn all of the features from scratch and learn how you can discover data patterns such as customer purchase behavior, pricing, and sales trends as well as customer demographics and geolocation analysis. You’ll become confident by upskilling yourself with Tableau because you will be the master of the art of turning data into critical information for key decision-making!

Elasticsearch

Finally, by the end of this learning track, take time and master the hottest big data technology on the market Elasticsearch! Elasticsearch is a powerful distributed, RESTful search and analytics engine capable of solving a growing number of use cases. Elasticsearch is one of the biggest players in the Big Data space since Hadoop. By adding Elasticsearch to your Resume or LinkedIn profile you’ll gain the attention of various companies investing in the technology. That is Awesome.

By the end of this track, you must learn everything there is to know about how to build an Elasticsearch engine and how to perform real-time analytics using accompanying tools: Logstash and Kibana, which make up the elastic search ecosystem.

All right, let’s sum up. When you complete this learning track you will have the skills such as SQL with knowledge of Oracle and Postgres databases, Python with Data Analytics and Visualization skills, Tableau for analyzing data patterns and decision making, and ElasticSearch for performing real-time analytics.

Time required to spend on data analyst path

Let me share the time frame that’s required to go through the track. Now it varies for different students as you may be a beginner or have intermediate knowledge in tech. So, don’t worry about it, focus on building your employable skills and you will land a good job.

So, I will recommend that you spend two to three months on the SQL language, one or two months on the Python language, and one month each on Tableau and ElasticSearch, that means that after five to six months of hard work and dedication you will be ready to help solve real-world problems using data and become a professional Data Analyst who is job ready!

So there you go. That’s the complete breakdown of the Data Analyst learning track with the time frame required to complete it. Go on and start learning with this newfound knowledge.

Resources

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