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We here at Dexlab Analytics believe in learning both theory as well as their application side-by-side. Thus, our students are given the opportunity to learn things from the ground up i.e. starting with the basics. Know more information at: http://www.dexlabanalytics.com/
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The steps to learn DATA SCIENCE Presented by DexLab Analytics
DexLab Analytics • Data science online training • Data science classroom training
We here at Dexlab Analytics believe in learning both theory as well as their application side-by-side. Thus, our students are given the opportunity to learn things from the ground up i.e. starting with the basics.
In this presentation as we move ahead will get the picture of how one can learn data science thoroughly to apply its theories in real-life practices.
Before we begin explaining how one can be a Data Scientist. Let us take a quick detour on how most successful data scientists began their career.
Many successful data scientists started learning coding at a very late stage. Most never knew coding before. Yet their passion for finding solutions and adding value to what they do enabled them to emerge successful.
The main problems with learning data science • The process of learning data science is a cloudy or amorphous process which is not completely aligned with application or practice.
By saying this we mean that there are two major problems to learning data science. The first being that it is unclear what the subject data science is about. And the second being that people do not know where to begin learning data science or how.
Also most often the resources that do exist about learning data science online lack the proper instruments on how they can be applied. • Many students stumble to apply what they have learnt as they understand the principles a little bit but are incapable of applying it by putting together the whole picture.
What is data science? • The first thing about data science is that this a very new term. And like all new things is widely misunderstood and cloudy in its true definition. • After all data in all forms is a term thrown about in science. So, what could be this new stream in science about data? • It is also the intersection point of several fields – statistics, machine learning, mathematics and computer science. • In a way data science takes some points from different existing fields and kind of juggles them together to give rise new methods, new meaning and newer applications. • But due to amalgamation of all these fields together, it becomes a little difficult to put data science in a box or tease apart its tissues.
What’s new in data science? • We can see increasing specialization of different roles within this field of data science: • Data analyst • Data engineer • Machine learning engineer • Programmer / Analyst
To be a successful data scientist you must be good at solving problems. This is the very fundamental for being a data scientist. Also there is no cut-and-dry method for problem solving in data science, one has to learn by doing i.e. in the process of working.
To bag an entry level job in data science, one needs: • Basic algorithm skills like logistic regression, linear regression and a couple more. • But you need be well-versed and must understand those algorithms in and out to actually finish a data-centric project with them. This is the only way you can expand your employability.
90% of data scientists work is data cleaning • Most of the work for data scientists is to figure out how to clean data and manipulate it. • While most people skip this step but it is crucial to get them done. • One must think over the steps why making a line chart is a better option in certain cases while a line chart in some others. • Thinking through tradeoffs when applying algorithm will make you a better data scientist.
And finally.. • You must be capable of articulating the results to explain it to non-data personnel • You must collaborate with different departments or teams of people and tell them the story behind your analysis – why you did, what you did, what was the result, what the visualizations suggest, what they should do to act on it and what will be the result. These are questions that must be explained to other team-members in a simple manner.
