Data Science or Data Analytics is the latest buzzword everywhere especially after Harvard Business Review categorized Data Scientist as “the sexiest job of 21st century”. Experts from diverse fields such as medical, chemistry or even politics are making the career shift towards data science. So much so that “how to become a data scientist?” is one of the most searched phrases on the internet. So if you are one of those individuals who are considering shifting their career towards data science then keep scrolling through this complete guide.
If you feel that being an efficient data scientist requires only programming skills, then let’s fix this misconception as soon as possible. Since data science is a multi dimensional field therefore you must be good at (statistics, probability, and linear algebra), database management, programming, data visualization, and machine learning etc. Refer to the diagram below for a detailed understanding of the data science landscape.
In order to better understand why this career is so hyped, we need to take a little time to consider the reasons for its popularity.
Following is the required skill set for a data scientist job however, the expertise in each field can vary depending on the nature of job or organization.
Probability, Statistics, and Calculus are the three highly used branches of mathematics in Data Science. A data scientist must have a solid grasp on mathematics to analyze or create data products because they are required to view data, patterns, or textures quantitatively. After the organization of data, a deep understanding of statistics is necessary for analysis and visualization. However, linear algebra is required only when dealing with big data and machine learning.
Algorithms are used for retrieving and manipulating data before analysis, which makes programming an indispensable skill for becoming a data scientist. R, python, julia are free to use, fast and efficient. Most importantly you can learn them easily using YouTube tutorials or free courses.
3. Machine Learning (ML)
Machine learning is the process of training a computer by feeding it new data so that it can learn and grow automatically. Nowadays it is heavily used for the improvement of artificial intelligence. By automating their processes using machine learning, companies are able to operate more efficiently and lower the cost of operations. Therefore, machine learning helps data scientists make systems that are able to make accurate predictions and make decisions instantly.
SQL is a programming language used for handling databases. It is also useful for the storage, access, and manipulation of data before its analysis. Creation of tables, querying, and SQL commands are very crucial for aspiring data scientists.
5. Big Data
Big data as the name suggests is the enormous amount of data collected from multiple sources that grows and varies exponentially over the time. Storing, retrieving, and manipulating such data is very challenging as none of the traditional relational databases are capable of sustaining it. Hadoop and Spark are the answer to this challenge. Hadoop is an open-source software framework for storing and processing big data.
6. Data Wrangling & Visualization
Data wrangling is the process of converting raw data into a more understandable and convenient format. Data Visualization is the representation of data using visuals such as statistical graphics, plots and information graphics. Both these skills are highly important while reporting your analysis as it makes numbers and digits more comprehensible for the human brain.
7. Business strategy
An expert in data needs to have an understanding of business strategy: the ability to formulate a business problem statement and conduct analysis based on it. This allows data scientists to set up their own systems that they can use to slice and dice data according to the specific needs of the organization.
In the case of those of you starting your university career, it might be smart to enroll in a bachelor’s degree program. Numerous universities across the world are offering 4-year long degrees in Data Science, Business Analytics, and Big Data etc.
1. Data scientist after graduation:
For those, who already have a degree in computer science, software engineering, or a degree with a solid background of maths, and programming can apply for a masters degree. Following universities are offering a Masters Degree in Pakistan.
2. Online masters in data science:
If you wanna go all crazy and get a graduate degree, then platforms such as Coursera and FutureLearn are offering Master’s Degrees in Data Science, Machine Learning, and Business Analytics from the top-class universities around the world. They have partnered with University of Michigan (ranked no. 5 in CS graduate programs in the US), HSE from Russia (ranked in top 200-250 universities of the world), and Imperial College London (ranked no. 1 in the world)
3. Data scientist without a degree:
If you want to expand your horizons, then you can enroll in online courses, and specialization tracks on websites such as datacamp,edX, CodeCamp, Khan Academy, and Udemy are also offering complete courses. World class experts from Google, IBM, Harvard, and Yale etc, are teaching these courses.
These platforms offer separate courses for Python, SQL, R programming for statistics, Big Data, and machine Learning etc. You can take several free crash courses on YouTube if you just need to revise one of these skills instead of paying for online courses.
Now that you have gathered all this information, let’s create a game plan that you can follow for a successful journey to become a data scientist.
If you are someone with a STEM background with sufficient knowledge of programming and maths especially statistics and linear algebra then shifting to data science might not be a big deal for you. But if not then don’t lose hope as you can also find courses that teach everything from very basics. There is nothing you cannot achieve if you pour your energy and time into it. All you need is a can-do attitude and creativity to figure out how to tie it into your current profession.