{"id":1610,"date":"2019-04-02T03:55:02","date_gmt":"2019-04-02T03:55:02","guid":{"rendered":"http:\/\/kusuaks7\/?p=1215"},"modified":"2023-07-12T12:09:34","modified_gmt":"2023-07-12T12:09:34","slug":"how-to-go-into-data-science","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/how-to-go-into-data-science\/","title":{"rendered":"How To Go Into Data Science?"},"content":{"rendered":"

Ultimate Q&A for Aspiring Data Scientists with Serious\u00a0Guides<\/h3>\n
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So\u2026you want to become a\u00a0data scientist<\/strong>? \u00a0You\u2019re a self-motivated person who is very passionate about data science and bringing values to companies by solving complex problems. \u00a0But you have ZERO experience in data science and have no clue how to get started in this field.\u00a0That\u2019s why this post is dedicated to you \u2014 enthusiastic and aspiring data scientists \u2014 to answer the most common questions and challenges faced by most people.<\/p>\n

If data is \u2018the new oil\u2019, then the data scientist functions much like an oil refinery, converting data into insights that can both save money and generate\u00a0capital \u2014\u00a0Eva\u00a0Short<\/a><\/p><\/blockquote>\n

All the questions below are not created by me, but you \u2014 the vibrant data science community. Please be noted that the questions below are not in order, therefore feel free to skip to any part of the questions where you find suitable.<\/em><\/p>\n

I hope that by sharing my experience in this post would shed light on how to pursue a data science career and give you some general guides to hopefully make your learning journey more enjoyable. Let\u2019s get started!<\/p>\n<\/section>\n

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What<\/strong> Is The Current Trend in Data Science Skills\u00a0Gap?<\/strong><\/h4>\n

The International Data Corporation (IDC) predicts that worldwide revenues for big data and business analytics will reach more than\u00a0$210bn<\/a>\u00a0in 2020.<\/p>\n

According to the\u00a0LinkedIn WorkForce Report in August 2018 for the United States<\/a>, there was a national\u00a0surplus of people with data science skills<\/strong>\u00a0in 2015. Three years later, the trend has changed tremendously in the opposite way as more companies are facing\u00a0shortages of people with data science skills<\/strong>\u00a0with big data being increasingly used to generate insights and make decisions.<\/p>\n

Economically speaking, it is all about SUPPLY and DEMAND.<\/p><\/blockquote>\n

The\u00a0good news<\/strong>\u00a0is: The \u201ctables\u201d are now turned. The\u00a0bad news<\/strong>\u00a0is: With rising job opportunities in data science, still, a lot of aspiring data scientists are facing challenges in getting their foot in the door simply because of their lack of data science skills gap relative to the requirements in the current job market.<\/p>\n

In the coming section, you\u2019ll see how to improve data science skills to close the \u201cgap\u201d, stand out among pool of other candidates and eventually increase your chances in landing your dream job.<\/p>\n<\/section>\n

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Questions<\/strong> &\u00a0Answers<\/strong><\/h3>\n
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(Source)<\/a><\/p>\n

1. What are the skill sets required and how to cover them\u00a0up?<\/strong><\/h4>\n

I\u2019ll be very honest with you. To learn ALL the skills sets in data science is next to impossible as the scope is way too wide. There\u2019ll always be some skills (technical\/non-technical) that data scientists don\u2019t know or haven\u2019t learned as different businesses require different skill sets.<\/p>\n

In general \u2014 in my opinion based on my experience and learning from other data scientists \u2014 there are some CORE skill sets that must be learned to become a data scientist.<\/p>\n

Technical skills.\u00a0<\/strong>Math and statistics, programming, and business knowledge. Despite an excellent proficiency in programming regardless of the languages used, we\u2014 as a data scientist \u2014 should be able to explain our model results to stakeholders in the language of business context and supported by math and statistics.<\/p>\n

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(Source)<\/a><\/p>\n

To learn math and statistics<\/em>\u00a0(or more comprehensive data science resources), check this\u00a0website<\/a>\u00a0out created by\u00a0Randy Lao<\/a>.\u00a0Randy<\/a>\u00a0has been helping aspiring data scientists and his repository on the website is truly a\u00a0gold mine<\/em>!<\/p>\n

I still remember when I first started out in data science I read this textbook \u2014 An Introduction to Statistical Learning \u2014 with Applications in R<\/a>. I highly recommend this textbook for beginners as the book focuses on the fundamental concepts of statistical modelling and machine learning with detailed and intuitive explanations. If you are a mathematically hardcore person, perhaps you would prefer this book:\u00a0The Elements of Statistical Learning<\/a>.<\/p>\n

To learn programming skills<\/em>, especially for beginners without prior experience, I\u2019d suggest to focus on learning one language (personally I prefer Python\u00a0since the concepts are applicable to other languages if needed and Python is more easier to learn.\u00a0The importance and and usage of Python or R\u00a0<\/a>has been a subject of debate in data science. Personally, I think the focus should be on how you can help businesses solve problems, regardless of the languages used.<\/p>\n

Finally, I can\u2019t stress enough that the\u00a0understanding of business knowledge<\/em>\u00a0is extremely crucial.<\/p>\n

Soft skills.\u00a0<\/strong>In fact, soft skills are more important than hard skills. Surprised? I hope not.<\/p>\n

LinkedIn surveyed 2,000 business leaders<\/a>\u00a0and the soft skills that they\u2019d most like to see their employees have in 2018 are: Leadership, Communication, Collaboration and Time Management. And I truly believe these soft skills play an essential part in data scientist\u2019 day-to-day work. In particular, I learned the hard way on the importance of communication skills which you can read it\u00a0here<\/a>.<\/p>\n

2. How to choose the right <\/strong>bootcamps<\/strong> and online courses when there are plenty of them out\u00a0there?<\/strong><\/h4>\n

With the hype surrounding AI and data science and many people jumping on the bandwagon, a lot of MOOCs, bootcamps, online courses, workshops (Free\/Paid) are mushrooming to hopefully not \u201cmiss the boat\u201d.<\/p>\n

There are many resources out there.\u00a0Be resourceful.<\/strong><\/p><\/blockquote>\n

So the question is: How to choose the learning materials that are\u00a0suitable<\/strong>\u00a0to you?<\/p>\n

My approach to filter and select the right online courses\/workshops for me:<\/p>\n