Data Science Career Paths

Data science is in demand and its growth is on steroids. According to Linkedin, “Statistical Analysis” and “Data Mining” are two top-most skills to get hired this year. Gartner says there are 4.4 million jobs for data scientists (and related titles) worldwide, 1.9 million in the US alone. One data science job creates another three non-IT jobs, so we are talking about some 13 million jobs altogether. The question is what YOU can do to secure a job and make your dreams come true, and how YOU can become someone that would qualify for these 4.4 million jobs worldwide.

There are at least 50 data science degree programs by universities worldwide offering diplomas in this discipline, it costs from 50,000 to 270,000 US$ and takes 1 to 4 years of your life. It might be a good option if you are looking to join college soon, and it has its own benefits over other programs in similar or not-so-similar disciplines. I find these programs very expensive for the people from developing countries or working professionals to commit X years of their lives.

Ok, so what one can do to become a data scientist if he/she cannot afford or get selected in the aforementioned competitive and expensive programs. What someone from a developing country like Pakistan can do to improve his/her chances of getting hired in this very important field or even try to use these advanced skills to improve their own surroundings, communities and countries.

We at Al-Nafi are on a mission to train the masses in Data Sciences, Artificial Intelligence, Blockchain, Cybersecurity and other emerging technologies.

Data is useless and can (and should) be misleading without the context. Data needs a story to tell a story. Data is like a color that needs a surface to even prove its existence, as color red for example, cannot prove its existence without a surface – we see a red car, or red scarf, red tie, red shoes or red something – similarly data needs to be associated with its surroundings, context, methods, ways and the whole life cycle where it is born, generated, used, modified, executed and terminated. I have yet to find a “data scientist” who can talk to me about the “data” without mentioning technologies like Hadoop, NoSQL, Tableau or other sophisticated vendors and buzzwords. You need to have an intimate relationship with your data; you need to know it inside out. Asking someone else about anomalies in “your” data clearly means you have no idea how your data in being generated, recorded or need to be analyzed in the first place.

Unfortunately, one of the most confused and misused words in data sciences filed is the “data scientist” itself. Someone relate it to a mystic oracle who would know everything under the sun, while others would reduce it down to statistical expert, for few its someone familiar with Hadoop and NoSQL, and for others it is someone who can perform A/B testing and can use so much mathematics and statistical terms that would be hard to understand in executive meetings. For some, it is visualization dashboards and for others it’s a never ending ETL processes.

For me, a Data Scientist is someone who understands less about the science than the ones who creates it and little less about the data than the ones who generates it, but exactly knows how these two work together. A good data scientist is the one who knows what is available “outside the box” and who he needs to connect with, hire, or the technologies he needs to deploy to get the job done, one who can link business objectives with data marts, and who can simply connect the dots from business gains to human behaviors and from data generation to dollars spent.

Here is a brief video on Data Science Career Paths one can choose from. One needs to have high school level understanding of linear algebra, calculus and statistics to get started.

Here are all the tracks with required technologies and earning potential. You can choose any track of your choice. Welcome to the world of Data Sciences