Data science by Pallavi Padhuu

Data science

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If you are considering some aspects of Data Science as a career progression, you may find it interesting to know that Microsoft has now launched a professional degree program for Data Science.

A job in data science is one of the most well-paid ones available, and data scientists are always sought after by even the largest company. Is it really possible to teach yourself data science? Can you go from just basic IT skills to becoming a master analyst? The answer is yes, provided you choose the right courses and take them with due diligence. Here we will present you with a roundup of the most important data science concepts you must learn to become a self-taught data scientist.

To master data science you need to learn how to solve various computational problems with algorithmic techniques. Algorithms are used to manipulate data through efficient data structures. You need to learn how to implement these structures in different programming languages, what to expect from them, and how to break large problems into more granular pieces. There are many strategies that must be learned to design an efficient algorithm, such as how to keep a binary tree balanced, how to resize a dynamic array, and how to solve problems recursively.

Data science is not rocket science, and like rocket science, while it may seem amazing when we see the end results, getting to the end result takes a lot of hard work. You might be surprised to learn that for the majority of data science projects, 50-80% + of the time is spent not running magic algorithms to predict the future or find patterns, but actually cleaning and preparing the data to enable it to be worked with optimally. Is it rare that we build a report using SQL that shows data from one single table? Usually, we are joining and combining and creating exceptions and other such combinations. With Data Science things are no different, and preparation of data is key.

Data Science is not all about the Guru, the Rockstar, the elusive 'Data Scientist' ... rather, it is about a tightly knit group of people working together in a team. It is very very difficult to find a single person who can do all the Data Science work that an organization will need. Normally, it is better to start to bring people together in your organization who have at least some of the skills needed and use these to form the foundations you can build upon. Data Science is not about chasing unicorns, it's about identifying what skills you have, what ones you need, and bring it all together. A good Data Science team will be overall, strong at data engineering/wrangling, SQL, visualization, ETL/cleaning, and will normally have at least one person who understands the different options available form an algorithmic point of view.  
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