Making a Successful Career in Data Science

Data Science, Analytics, Artificial Intelligence, Machine Learning; nowadays, we keep on hearing these terms quite often. There is a lot of glamour associated with them. Many are aspiring to make a career in Data Science. Why data science is gaining so much popularity?

It is said that today’s world is of data and information. We produce and consume so much data. For example, we use social media. We cannot imagine a day without a mobile phone and without social media- Facebook, Twitter, YouTube, Snapchat, WhatsApp, Pinterest, and so on. People are using social media for sharing – photos, videos, documents, communicating, commenting, etc., Almost every business creates a website. Data consumption is increased tremendously after the exponential growth of Mobile phones. And this will keep on growing further, thanks to more advanced technologies like the Internet of things(IoT).

Every day, roughly we create 2.5 quintillion bytes of data. With the growing popularity of IoT, this data creation rate will become even greater. – seedscientific.com

Career in Data Science: More and More Data

Here are some interesting numbers to give you an idea of the data we end up generating every day, every minute, and every second.
• Every minute, Google does 5,700,000 searches
• Every minute, Instagram users post 65,000 photos
• Every minute, YouTube 694000 Hours streaming video
• Every minute, Zoom hosts 856 Minutes of Webinar
• Every minute, Snapchats shared 2000000 chats
And the list continues…

That is really mind-boggling! We are aware that now we use so much data daily. But why we are discussing all this? What can be done with this data? Is it really useful to study this large data to get something interesting for business?
And the answer is a big ‘YES’. Let us look at what it means by studying this data or the science of it.

Career in Data Science, understand what it is!

When you visit search on the internet or talk to people, you will hear the following terms very often. These terms which are in fashion right now are data science, data analytics, machine learning, deep learning, big data, etc. Without really understanding the meanings of these terms, it is not possible to understand data science.

Let me simplify this for you from the perspective of the job roles or tasks. To process a very large volume of data to discover new insights is the main function of Data Science. Data warehousing, Data mining were the terms used for this kind of computing. However, in recent years it got more glamourized in the name of data science or machine learning.

We can call Data science the big domain which encompasses everything. In this domain, at a very high level, there are two types of tasks: one is to clean and prepare the data and the other is to analyze the data for insights.

Engineering the Data

An important step before analyzing the data is to clean it and prepare for the next step. It is called Data Engineering. Data from various sources is collected, processed, cleansed, transformed, and stored in different ways. This includes everything from the file system, databases, data warehouses, and NO-SQL databases. In addition, the big data processing mechanisms like Map Reduce, PIG, HIVE, etc., data wrangling also comes in this category.
To process data, you must have skills in Databases, SQL, and Programming languages like Python and/or a set of tools that are part of a big data system like Hadoop.

Data Analytics

This section contains tools and technologies which are used to process and view data in different ways. Statistical analysis is done using different statistical tools and algorithms. These generate insights or patterns. Using these insights, businesses can take vital decisions like how much to order, how to arrange goods on the shelf of a big store, and whether to give a loan or not to a particular applicant? This is what you learn in a data science certification course.

 

Learning Data Science: Python/R Programming

Data engineering being more focused on data processing or programming may be of more interest to computer graduates. On the other hand, data analytics being statistically oriented may attract students from other science disciplines. But the bottom line is one has to have a fair knowledge of both to do well in a career.
Starting step could be learning Python and R Programming. Recently Python has become popular because of the availability of a large number of readymade libraries for data science algorithms. If you want to pursue a Data science course, make sure that it teaches you statistical concepts, Python/R Programming, and then various machine learning algorithms with a project.

Why wait, take a step and enroll in a Python Programming course at Texceed!