How to make a career change in Data Science

“Data Science is called a technology-focused career”

Data Science has attained a high position in the IT industry. Data Science is most commonly collaborated with programming languages like Python or R. You must have heard that Data Science is a fast-growing field and you’ll be able to get better remuneration. Well, all that you have heard about the growing opportunities in Data Science is all true! But if you are also planning for a career change in Data Science, we would recommend to not rush to learn Python and R Programming languages. Instead, leverage the expertise in the domain you have assembled so long. In this blog, we’ll share some prospects of the career change in Data Science.

How Data Science is the best to choose?                     

Following are the top reasons that will answer this question:-

Reason 1: Many companies are now hiring data scientists and everyone is aware that there is a structural scarcity of qualified data scientists. Due to this reason, companies are unable to find the above average data scientists.

Reason 2: Data Science is the hottest and among top listed job profiles in the IT sector. According to Forbes, the average of 2,900 unique job postings was done in the year 2016. McKinsey Global Institute also gave prediction in the past of almost 200,000 job postings of data science in the year of 2018 which was true also. Future of data science is beyond that!

Reason 3: Data Science is everywhere and will stay long in the future. Data science is used in areas like Banking, Finance, etc and in these areas there is a huge necessity of data analytics. So, data science is going to stay long in the market.

Who is a Data Scientist?

We have discussed the top reasons to switch your career in data science. Now, let’s look find out what exactly is a data scientist? What exactly does he/she do? Does data scientist play with chemicals like other scientists? Or he is working in NASA to perform advanced-level experiments? Well, do not confuse yourself with the post-fix of the term “scientist”. A Data scientist is not like the other “scientists” in NASA who is creating missile. But Data Scientist is the researcher with an analytical mind who analyzes data to extract actionable insights from the data. A Data scientist is the one who:

  • Regulates the right datasets and variables
  • Recognizes the most challenging problem of data-analytics
  • Gathers large sets of structured and unstructured data from different resources
  • Creates and applies algorithms to the storage of Big Data
  • Interprets data to find solutions
  • Analyzes data to concede patterns and trends
  • Communicates findings to the stakeholders by using various tools of visualization, etc.

“Beginning of this thousand-mile journey may seem tough at the beginning, but once you get yourself in, you will be contented and pleasured.”

Various job titles available in Data Science

  • Data Analyst

It is an entry-level position in which your job is to reckon the company’s data and use it to solve the queries of the business. The average salary of a Data analyst as per Indeed is $68,752

  • Data Scientist

The responsibilities of a data scientist are similar to the data analyst. But the difference is that they typically create a machine learning model which provides accurate predictions about the future based on the past data. The average salary of a Data scientist as per Indeed is $128,173

  • Data Engineer

The primary responsibility of a data engineer is to manage the whole data-based infrastructure of the company. This job title requires a lot more software development and programming skills, but less statistical analysis. The average salary of a Data engineer as per Indeed is $132,653

  • Machine Learning Engineer

The Machine learning Engineer is particularly a person who has expertise in machine learning rather than software development. The average salary of a machine learning engineer as per Indeed is $144,085

  • Quantitative analyst

Also known as “Quants” are the people who use advanced statistical analysis to answer the questions related to finance and risk. The average salary of a quantitative analyst as per Indeed is $142,049

  • Data warehouse architect

The person with the specialization of data engineering who takes in charge of the data storage systems of the company is known as a data warehouse architect. The average salary of a Data warehouse architect as per Indeed is $136,151

  • Business Intelligence analyst

This is the person who is majorly focused on market analysis and business trends. The BI Analyst makes use of various data analysis tools like Microsoft Power BI to analyze the data. The average salary of a business intelligence analyst as per Indeed is $90,150

  • Statistician

A Statistician is the replacement of the title “data scientist”. The main job of a Statistician is to be familiar with the Mathematical concepts and principles that underlie a machine learning model. The average salary of a Statistician as per Indeed is $87,021

What do I need to become a Data Scientist?

Ask yourself the following questions before you start thinking in Data science:-

  • Do you have knowledge in the data-intensive domains like retail, banking, insurance, telecom, media, health care, etc.?
  • Do you have experience in the domains that are strapped with analytics like data warehousing, reporting, dashboarding, market research, etc.?
  • Are you from a technical background or have knowledge of Excel, Python, analytics software, Perl, etc.?
  • Does your current job involve dealing with data and information like forecasting, pattern detection, anomaly identification, etc.?
  • Does your current job have various skills like project management, team management, preparing and delivering pitches, etc.?

If the answer to all the above questions is “YES”, then go ahead in this ocean of data science as you have developed or developing relevant skills.

Now, let’s move forward!

There are numerous articles available on the internet to help data scientists hone the skills. All these noises make it challenging for the data science aspirants to know where to invest their time as they are looking for the transition into the field. Remember, there is no one-size-fits-all solution for everyone. Everyone is unique, so as their skills and capabilities.

Before proceeding further in the field of data science, understand your fit to an analytics role by testing your own analytical skills. The first thing you have to do is to learn Python. For this, you may start taking MOOC and create basic projects. Then, get more familiar with data pipelines and data manipulation skill set. Machine learning is the next important part to enter in. Remember, SQL is also a must. Check for the certifications available in Data Science. Certifications also play an important role in getting a new job or in the career transition. It really adds value in your resume!

Skills required for the Data Science

Following are the technical skills of a data scientist:-

  • Statistical skills
    • Descriptive statistics
    • Inferential statistics
  • Probability skills
  • Mathematical skills
    • Linear algebra
    • Calculus
    • Discrete Math
    • Optimization theory (extremely important in data science)
  • Programming skills
    • Python
      • Pandas
      • Matplotib
      • Numpy
      • Scikit-learn
      • TensorFlow
    • R
      • ggplot2
      • dplyr
      • purr
      • shiny
    • Tableau
    • Database query languages (SQL and NoSQL)
    • SSAS (SQL Server Analysis Services)
    • Big Data technologies
      • Apache Hadoop
      • Apache Spark
      • Apache Flink

Following are the non-technical skills of a data scientist:-

  • Data curiosity or inquisitiveness
  • Expertise in business
  • Communication skills
  • Teamwork
  • Data Visualization
  • Data Wrangling
  • Capability to work with unstructured data
  • Capability to understand analytical functions

What is the right time to make a career change in Data Science?

So, putting all your eggs in one basket, you can make a career transition in the field of Data Science. This will not only raise your current job but will also help you get better paychecks. Making a long-jump is risky. Stress and struggle can be avoided by starting from small scale before you take the full-time employment plunge. Adding the above-mentioned data analytical skills will make you add value in some way. Ask yourself these questions before making a career transition in data science:-

  • Am I ready for the job in Data Science?
  • Am I ready to speak and think like a data scientist?
  • Am I a highly logical minded person?

If the answer to these questions is “Yes” then congratulations, you are now only a few steps closer to finding and landing your dream job in data science. Enroll yourself in the Data Science training now. Happy learning!  

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