How Can I Start My Career in Data Analytics?
If you are an individual who loves to play with numbers, then a career as a Data Analyst could be a perfect fit.
Data Analyst is one of the most sought-after jobs globally in recent times, and the demand for Data Analysts is skyrocketing. So, if you are also keen to start your career in Data Analytics, this is the right time to move with the Data Analytics course in Bangalore. This course will help you learn and understand the tools and techniques, working with databases, programming languages R and Python, predictive analysis, and statistics.
Well, that’s quite interesting. But, how are you going to start your career in Data Analytics? So, if you have decided on a career in this in-demand field, here’s how to get started.
How Do I Start a Career in Data Analytics?
You can find a Data Analytics job across every industry, and certainly, there’s more than one path towards starting a career in this booming field. Whether you are just starting your career in the data world or moving to a new career, here are some steps that you need to take to start your career in Data Analytics.
Six Important Steps To Start Your Career in Data Analytics
Step 1 – Learn R and Python
Getting a job in Data Analytics typically requires having a good grasp of technical skills, which usually starts with programming languages such as R and Python. Python and R are the two most commonly used languages in the data space.
So, if you are just starting, I strongly suggest you start learning Python, as it is easy to pick up and more user-friendly than R. As Python has more libraries than R, it is more widely used. It also broadens your gate to move in a field like Machine Learning and web development.
R is a free open software environment for statistical computing and graphics, widely used by statisticians and data miners for data analysis and developing statistical software.
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Step 2 – Learn SQL
SQL – Structured Query Language is one of the necessary skills to get a job in Data Analytics. In a daily job, one would usually require working on a large amount of data from a database and manipulating the data according to the requirements of the business.
Many organizations integrate SQL with other frameworks, and one needs to know how to query data using these frameworks. SQL can be used within multiple languages Python, Hadoop, and Scala, and it will differ depending upon the company you are working with.
Step 3 – Data Analysis and Visualization
One of the biggest truths about a career in Data Analytics is that Data Analysts never approach a problem or question with a preconceived mindset or answer. Instead, a Data Analyst uses data to conclude; one needs to know how to analyze data, derive insights, and present the insights derived from the data so that your team member and stakeholders can easily understand and make business decisions based on insights you present.
Now, the insights are usually presented with the help of Data Visualization tools like Tableau, Looker, Power BI, etc.
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Step 4 – Build Your Portfolio and Storytelling
Now, you have the necessary skillset required to start a career in Data Analytics.
But that’s not it; now you need to present these skills. Whether you are from a technical or non-technical background, you must show that you have the required skills to become a Data Analyst.
And to do this, you just need to build a portfolio, let’s say an e-portfolio. Now, you may ask how to build an e-portfolio? Well, here you go –
- Building dashboard on the tableau
- Using Python to analyze Kaggle datasets
- Visualize your findings through charts, maps, and graphs
- Clean and normalize raw data
- Scrape data from different sources
Here are a few examples that you could display in your portfolio:
- Scrape music data from Spotify or other music platforms and identify top-performing artists
- Use Glassdoor or Payscale data to identify regions with the highest and lowest salary offered
Showcasing your projects on your professionally written resume will give a great impression and help you stand out from the crowd.
Now, coming to storytelling, it’s basically an art. An art of connecting dots. So, make sure you document every step you took to create the project and write a brief note about it. And the best way is to start your own blog and publish articles based on your learning and experiences.
Step 5 – Work on Real Data and Projects
To learn and understand any concept or technology, you should deal with it in real time. Therefore, you can look for any certification course or degree program that includes hands-on projects based on real-life data.
You can also apply for an internship; you can start gaining real-world experience and apply what you learn with an internship opportunity. Moreover, these internships add value to your resume.
Step 6 – Networking!
Networking is an essential part, and it is always going to be crucial when you are planning a career advancement. However, your focus should initially be only on learning rather than networking, as it may divert you from your goals.
Expanding your network is important, and you could start it from;
- Creating an account on LinkedIn and adding professionals who are in the data space
- Attending industry events
- Participate in quizzes and hackathon
- Find experts on Quora and reach out to them
- Join community forums
It’s said that whether you love it or hate it, networking helps, but do not waste too much time on it.
Data Analytics is a good place to start if you look to break into the data space. These steps would help you achieve your final goals, but always remember career transformations take time, and there’s no such shortcut to start a career in Data Analytics.
One of the best ways to get started and prepare yourself is to go for further education, whether through a university degree, online boot camps, or any other courses as per your comfort.
The demand for skilled data professionals is huge, and organizations are investing a huge amount of time and money on Data Analysts, Data Scientists, and Data Engineers, so take the right steps that lead to your career growth.