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What are the skills required for Data Science? | Know more about it

Last updated on 28th Jan 2023, Artciles, Blog

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Karthika (Data Engineer )

Karthika has a wealth of experience in cloud computing, including BI, Perl, Salesforce, Microstrategy, and Cobit. Moreover, she has over 9 years of experience as an engineer in AI and can automate many of the tasks that data scientists and data engineers perform.

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    • In this article you will learn:
    • 1.Essential Skills for a Data Science.
    • 2.Machine Learning.
    • 3.Data Visualization & Communication.
    • 4.Conclusion.

Essential Skills for a Data Science:

Programming Skills:

No matter what type of a company or role on interviewing for you’re likely going to be expected to know how to use the tools of the trade. This means a statistical programming language like a R or Python and a database querying language like a SQL.

Statistics:

A good understanding of statistics is vital as a data scientist. Should be familiar with the statistical tests, distributions, maximum likelihood estimators etc. This will also be a case for machine learning but one of the more important aspects of a statistics knowledge will be understanding when various techniques are (or aren’t) a valid approach. Statistics is important at all the company types but especially data-driven companies where stakeholders will depend on a help to make decisions and design / evaluate experiments.Math and statistics are two of the most powerful tools that a data scientist can use to do their job. As a data scientist you won’t just use complicated methods like neural networks to figure out what to do. Every data science beginner starts with simple linear regression analysis which is also a type of machine learning algorithm. One of the most important first steps in data science is to put the data on a chart and figure out what it means.A basic visualization like the histogram or a bar chart just gives some high-level information but with the statistics data scientists get to work with data in an information-driven and targeted way. The math involved in performing technical analysis of data helps to draw concrete conclusions rather than just guesstimating. Having a good foundation in math concepts like rational and irrational numbers helps data scientists to write accurate and efficient code.Following are basic math and statistic concepts of every data scientist must know:

  • Statistics and probability theory.
  • Probability distributions.
  • Multivariable Calculus.
  • Linear Algebra.
  • Hypothesis testing.
  • Statistical modeling and fitting.
  • Data summaries and descriptive statistics.
  • Regression analysis.
  • Bayesian thinking and modeling.
  • Markov Chains.
Skills for Data Scientist

Machine Learning:

If at a large company with big amounts of data or working at a company where a product itself is especially data-driven (e.g. Netflix, Google Maps, Uber) it may be the case that you will want to be familiar with machine learning methods. This can mean things like k-nearest neighbors, random forests, ensemble methods and more. It’s true that a lot of these techniques can be implemented using R or Python libraries—because of this it’s not necessary to become an expert on how algorithms work. More important is to understand broad strokes and really understand when it is appropriate to use a different technique.As artificial intelligence and predictive analytics are two of the topics in a field of data science an understanding of machine learning has been identified as a key component of an analyst’s toolkit. While not every analyst works with machine learning the tools and concepts are important to know in order to get ahead in a field. Will need to have statistical programming skills down first to advance in this area however an out-of-the-box tool like Orange can also help to start building machine learning models.

Multivariable Calculus & Linear Algebra:

Understanding these ideas is most important at companies where a product is defined by the data and where small improvements in predictive performance or algorithm optimization can lead to big wins for a company. In an interview for a job as a data scientist you might be asked to use some of the results from machine learning or statistics elsewhere Or the interviewer might ask some basic questions about multivariable calculus or linear algebra which are the foundations of many of these techniques. And you might wonder why a data scientist needs to know this when there are so many ready-made solutions in Python or R. The answer is that at some point it may be worth it for a data science team to build their own implementations in-house.

Data Wrangling:

Often data analysis is going to be messy and difficult to work with. Because of this it’s really more important to know how to deal with imperfections in data. Some examples of a data imperfections include missing values inconsistent string formatting (e.g. ‘New York’ versus ‘new york’ versus ‘ny’) and date formatting (‘2017-01-01’ vs. ‘01/01/2017’, unix time vs. timestamps, etc). This will be most important at small companies where an early data hire or data-driven companies where a product is not data-related (particularly because the latter has often grown quickly with anot much attention to data cleanliness) but this skill is important for everyone to have.

Data Visualization & Communication:

Visualizing and communicating data is incredibly important especially with young companies that are making data-driven decisions for the first time or companies where data scientists are viewed as people who help others make data-driven decisions. When it comes to communicating this means describing findings or the way techniques work to an audience both technical and non-technical. Visualization-wise it can be immensely helpful to be familiar with data visualization tools like matplotlib ggplot or d3.js. Tableau has become a famous data visualization and a dashboarding tool as well. It is important to not just be familiar with the tools necessary to visualize data but also principles behind visually encoding data and communicating information.

SQL:

SQL or Structured Query Language is a common industry-standard database language and a data analyst may need to know it more than any other skill. People often think of the language as an “upgraded” version of Excel because it can handle large datasets that Excel can’t.Almost every company needs someone who knows SQL to manage and store data connect multiple databases (like the ones Amazon uses to suggest products you might be interested in) or build or change the structures of these databases. There are thousands of job ads posted every month that require SQL skills and the median salary for someone with advanced SQL skills is well over $75,000. Even people who aren’t very tech-savvy can benefit from learning this tool. If you want to work with Big Data you should start by learning SQL.

Machine Learning

Problem Solving:

Problem-solving is the most critical data science skill because data science is all about solving challenging business problems. Without business problems there wouldn’t be a need for the data scientist. As a data scientist it does not matter what technology or programming language you can use if you cannot solve business problems you won’t be very good at developing algorithms for the same. I constantly hear complaints about job interviews that are too complex to crack because they ask a candidate to solve some difficult business cases at hand to test a candidate’s ability to solve problems.

Conclusion:

Data science is an umbrella term that encompasses data analytics, data mining, Artificial Intelligence, machine learning, Deep Learning and several other related disciplines.

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