Data Scientist: Everything You Need to Know

Data Scientist: Everything You Need to Know

Every day, 2.5 million Terabytes of data are generated by the 6 billion connected devices to the internet. Millions of more devices will be connected by 2020, generating an estimated 30 million terabytes per day.

This should be interesting to you, whether you are a newbie or an IT professional. You’re probably aware of the mass layoffs in India’s tech companies if you’ve been following the news. At this moment, it is essential to re-skill in Data Science.

These are 25 facts you need to know if you plan to make the switch to Data Science. Continue reading!

  • First, the Harvard Business Review names Data Science the 21st-century’s hottest job. Data science is praised by domains from many industries for the numerous business insights it provides. Gautam Tambay (co-founder and CEO at Springboard) also believes data is the new oil. The number of people who use the internet to find information has increased dramatically over the past decade. This has made it possible for all basic tasks to be done online. Data Science, with so much data being produced each day, is the field that can help businesses find crucial business data and get them on track.
  • Data scientists are in high demand today. The US is the largest market for data scientists, with 190,000 needed by next year. India joins the elite group of data scientists, and requires data scientists in a variety of industries. India’s Big Data analytics industry is expected to grow eightfold by 2025, to $16 billion.
  • Data Science, for the uninitiated, is the process of taking large amounts of data and processing it to find meaningful information. This can be used to help businesses gain insights into customer experience, concerns, and other important aspects that could complement their business operations.
  • You generate tons of data each day. This data is used to optimize your performance, whether you are using your GPS to find a local destination or your online shopping app. Have you noticed how Amazon’s app keeps recommending the best products as you use it?
  • Data science requires that you have or improve your skills in statistics, data-science tools, communication skills, and a commendable understanding of quants. Data scientists use all of these skills to analyze data, identify patterns, discover angles, analyse it, and extract useful information.
  • It doesn’t matter if you have a degree or a PhD. To be a data scientist, you need to understand the basics of analytics. To get started, you will need to be able to use analytics tools and understand basic data processing.
  • Each company approaches data science differently. Data science is complex and it is impossible to be an expert. Knowledge in widely accepted technologies such as SAS/R, Python programming, SQL database, and Hadoop platform would be helpful.
  • Data scientists make more than average IT workers.
  • Both start-ups as well as tech companies prefer data scientists. It’s startups who are becoming more aware of data science and looking to hire more data scientists. Tech companies and corporations are following the trend of reinvesting in analytics and data scientists.
  • Automation isn’t the main reason tech companies lay off workers. It is the huge difference between evolving technology and the insufficient manpower to use it. Data science requires specialized skills, and the talent pool that has not been able to acquire these skills is being cut.

Analytics can be divided into three broad categories: descriptive analytics, predictive analysis, and prescriptive.

When you analyze a set of data and then describe what you find, you call it descriptive analytics. If you look at your bank statements for the past month and find that 30% was spent on rent, 20% on food, and 10% on fuel, this is descriptive.

Predictive analytics refers to the ability to forecast or estimate using historical data. You can use your bank statements from the past 12 months to predict your monthly expenses.

Prescriptive analytics can be used to identify the best way to reduce your spending. Prescriptive analytics can help you determine the most efficient category to cut your fuel and food expenses.

Some data online may not be crucial. Dark data is data that cannot provide meaningful insight. These data can range from logs in a call center to social media feeds.

Data scientists need to be familiar with the term Machine Learning. Machine Learning is simply the creation of systems that are able to learn from the data they receive. Machine Learning is evident in Google Maps and Siri. Siri responds faster by finding patterns in your queries and responding better, as you may have noticed. Google Maps also becomes optimized and provides predictive insights about your destination.

You must master key algorithms in Machine Learning. You will need to master algorithms such as Random Forest, NeuralNets, SVM, and Logistic Regression.

R is one of Data Science’s most used programming languages. If you don’t know R, you can’t be called a data scientist.

Data Science has two types of data: structured and unstructured. Structured data can be classified, segmented, and placed into databases. Unstructured data cannot. Unstructured data includes social media posts, books, and audio recordings.

IoT, the latest technology in Data Science, is a major contribution to that field. IoT is the term for the network of devices that are connected via the internet. The IoT ecosystem includes smart homes, smartwatches, and health gears. Surprise, even! There are smart breweries.

Data science is closely linked to IoT as it is both about data generation and Data Science about analyzing it. You will be able to update your skills and be part of the next big tech revolution by becoming a data scientist.

It is not enough to learn Data Science. What is even more important is actually practicing it. Make sure you have enough data sets and case studies to work on if you are interested in a Data Science course. It’s not just about the theory; it’s what you do with your hands.

Data is never perfect. Be aware that cleaning your data will take more time than creating insights. Only after the data is cleaned can you begin to do analytics.

Data Science requires more than technical skills. It also requires communication skills. Pros have an in-depth understanding of the insights extracted. A layman will be confused if he or she sees your discovery. You must communicate your ideas clearly and be skilled at creating spreadsheets, presentations, and documents.

Data Science is a rewarding job. Data Science is a field that welcomes talent at a time when many employees are being laid off, cut in pay, or getting pink slips. Data Science will allow you to not only be an authority in your company or organization, but also provide a great work-life balance and a good salary.

These are the most important facts you need to know about Data Science. So what are you waiting for? Start Data Science now and make a career change to high-paying data science careers.

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