At a time when news about IT layoffs has become part of everyday conversation and continuous learning has become the tech du jour, the need to pivot to advanced skills has become even greater. But besides job uncertainties, the question most mid-level and senior professionals are grappling with is how and where to get started. While data and analytics has emerged as an attractive option for those looking for a career advancement and career change, job seekers have little knowledge on how to get a head start.

Time to introspect to make an effective transition:

1. Do I have an affinity towards numbers?

The first question you need to ask is whether you are made for this career. You need to be passionate about statistics and numbers to break into this industry.  For an absolute beginner, introductory courses on statistics is a good start. R is the go-to tool for statistical modelling and there are a plethora of free resources to get started on it. For early-stage professionals, in depth knowledge of R and Python or SAS is a must. Here’s a free online test to assess your skills for data and analytics.

2. Do I have enough time to devote for new learning?  

While there are plenty of training options to obtain analytical skills and grow your career, you have to be open to set aside dedicated time for learning.

Data Science is essentially a combination of 3 skills: Statistics/ mathematics, coding/ database knowledge & Business acumen. Making a good start can make a lot of difference in this field. One has to give at least 6-12 months time for dedicated learning of data analysis and statistics and get a top view on Big data, Machine Learning and NLP to get a good start in this domain. Here a few good sources to get started. Our research indicates learning data science is a big commitment and one needs to devote at a minimum of 10 hours a week for up to nine to 12 months.

3. At what point am I in my career?

Another key question to consider is at what point are you in your career during the transition.  Graduates and early stage professionals should have expertise in SQL, Python and in-depth understanding of statistical concepts. Learning on the job provides a great opportunity for career growth. For beginners, exposure and experience in an data science role will help develop a better understanding of solving business problems.

• Mid-stage (5-10 years): At this stage you are already working in a particular industry and chances of finding a job outside the domain may be slim. It would be better to transition to an analytical role within your company.
• Managers/Practitioners (10+ years): Chances are you already working as a Project Manager or a Team Lead and looking for ways to add to your skillset. There are plenty of executive programs to get you started.  Good domain knowledge coupled with knowledge of R/Python and SAS is a must.

4. What career path do I want to follow?

Most professionals come from IT background with strongly coding skills. The data science field offers three career paths: Data Engineering, Business analyst and Data Scientist. Most IT professionals pivot towards the data engineering roles and sharpen Hadoop, Spark, SQL, NoSQL. Data Analysts may come from MBA or non-MBA backgrounds such as finance and marketing. They combine visualization skills (Tableau, Qlik, Power BI) backed by querying language (SQL, Pig, Hive) and Python. Data scientists have a  combination of statistics, mathematics and coding skills backed by domain knowledge to deliver business-oriented results. Professionals with degrees in related field such as math, computer science, statistics also make for good data analysts.

One of the chief requirements for a data analyst is to translate data into a viewable/understandable format. Of late, professionals from marketing/finance/research background who are able to communicate complex ideas carefully, have successfully transitioned to Business Analytic roles.

5. What should I do to get started towards this transition?

Now that you know this career is right choice, you have to get started. Even though there are plenty of free resources, it can be exhausting to chalk out a clear learning path. For starters, there are plenty of self-paced learning plans that tackle a) fundamentals of data science b) short term courses that fill a specific gap (such as Data Wrangling with MongoDB & Data Analysis with R) c) specialized courses on Python, R programming.

If you are looking for specialized resources, we got you covered. Check out these data science communities to keep abreast with this growing field:

Communities: Datatau, Cross Validated, Reddit Machine Learning Subreddit

Datasets: Kaggle Competitions, Reddit, Machine Learning Repository, Open Data Platform

Free Books: Think Python by Allen Downey, Think Stats by Allen Downey, An Introduction to    Statistical Learning by James, Witten, Hastie and Tibshirani, The Data Analytics Handbook, Data Driven: Creating a Data Culture by DJ Patil and Hilary Mason