Top 10 Skills Required for a Successful Data Scientist

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Curious to know which skills are required for a successful data scientist? We’ll discuss them here.

Data science is one of the most sought-after career streams these days with the rise of big data. Glassdoor currently ranks “Data Scientist” as the third best job in the United States.

data scientist top three

This is not surprising as every company is looking for competent data scientists to give them the edge to succeed in this competitive business scenario. To understand this high demand for data scientists, let’s take a quick look at, ‘What does a data scientist do?’

A data scientist extracts actionable information from data to help businesses make informed decisions. By gathering, compiling, and interpreting data, a data scientist helps in making action plans.

Now the big question is how to become a successful data scientist or what are the skills required for a successful data scientist. Let’s find out.

Top 10 skills required for successful data scientist

Technical skills

Let’s start with the technical skills required for a successful data scientist.

1. Probability & statistics

The most important skill required for a successful data scientist is proficiency in probability and statistics. Statistics lies at the heart of data science and you can’t possibly proceed without having your basic concepts crystal-clear. 

Thorough knowledge of statistical concepts like mean, median, mode, etc., is necessary for data representation and deriving insights from it. 

Probability concepts like skewness, kurtosis, hypothesis testing, confidence intervals, etc., are essential for interpretation too.

A good grasp of these concepts enables a data scientist to

  • Explore and understand the data
  • Identify the relationships that may exist between two variables
  • Predict future trends based on the previous data.
  • Determine patterns in the data
  • Identify anomalies in data

2. Multivariate calculus & linear algebra

We take into account many unknown variables or predictors to design data science models. This makes calculus and algebra keys to data science and machine learning.

Proficiency in the following concepts is an essential skill required for a data scientist:

  • Derivatives and gradients
  • Step function
  • Sigmoid function
  • Logit function
  • Rectified linear unit function
  • Cost function
  • Plotting of functions
  • Minimum and maximum values of a function
  • Scalar
  • Vector
  • Matrix
  • Tensor functions

3. Programming, packages, and software

Data science and machine learning have grown immensely because of the boost in computing power.

Programming provides us with a way to communicate with machines. So, to create practical applications successfully, a data scientist needs programming skills.

Knowledge of programming languages such as Python, Perl, C/C++, SQL, and Java, with Python being the most preferred, is a very important skill required for a successful data scientist. 

But does a data scientist need to become the best at programming? 

Not at all, but they definitely need to be comfortable with it.

4. Data wrangling

Quite often, there are many imperfections in data that need to be resolved. This is why data wrangling is an important skill required for a successful data scientist. It is the process of cleaning and unifying unstructured and scattered data for easy access and analysis.

With data wrangling, data scientists provide a very accurate representation of actionable data, which reduces the time required for data processing and response. It also reduces the time spent collecting and organizing unruly data before it is used. 

5. Database management

Since data scientists spend most of their time preparing the data for processing, it is very important for them to know how it will be managed so that they process it in the best way possible.

The database management skills allow data scientists to:

  • Edit, index, and manipulate databases
  • Define rules for validating and testing data
  • Enable the support for a multi-user environment to enable access and manipulation collaboratively
  • Perform manipulation of data, field name, file structure, or even data formats

6. Data visualization

Just as the name suggests, data visualization represents information and findings primarily through graphs and charts. They help data scientists communicate the results in an easily understandable manner.

Generally, the following are used for data representation:

  • Histograms
  • Bar charts
  • Pie charts
  • Scatter plots
  • Line plots
  • Time series
  • Relationship maps
  • Heat maps
  • Geo maps
  • 3-D plots

A knowledge of some graphic design solutions or using Premiere Pro presets can also come in handy to make such charts and communicate insights in an interactive manner.

7. Machine learning/deep learning

With the decision-making process becoming data-centric, machine learning is among the core required technical skills required for a successful data scientist. It helps in data modeling and obtaining results.

Machine learning for data science ranges from simple linear and logistic regression models to advanced ones like Random Forest, XGBoost, CatBoost, and so on.

For instance, a data scientist can use the power of AI and ML for Airline Route Planning, Automatic Spam Filtering, Facial and Voice Recognition Systems, AI Chatbots, Improved Interactive Voice Response, etc. 

8. Cloud computing

Cloud computing is an important skill for a successful data scientist as it goes hand in hand with data science. A data scientist with cloud computing skills can use platforms such as Google Cloud, AWS, and Azure that provide access to databases, frameworks, programming languages, and operational tools.

Doing so can help with data acquisition, wrangling, transforming, analyzing, sanitizing, mining, validating, and testing predictive models. It also helps in tuning the data variables and optimizing model performance.

Non-technical

Let’s have a look at the non-technical or soft skills required for becoming a successful data scientist.

9. Strong business acumen

A successful data scientist needs to fully understand the key objectives and goals of the business. It’s only then that they’d be able to find solutions to achieve those goals. 

Also, the solutions provided should meet the goals in a way that’s cost-effective, easy to implement, and ensures broad adoption. 

For example, logistics or fulfillment is a major concern for the ecommerce domain. While companies struggle to decide whether to use third-party fulfillment or not, data scientists can give insights based on previous data and future predictions to ease this decision-making process.

10. Great data intuition

This is perhaps the most important non-technical skill a successful data scientist would need. Data insights are not always easily perceptible, especially in large data sets, and a successful data scientist needs second sight to know when and where to look for valuable information. 

This makes data scientists more efficient in their work. However, gaining this skill requires experience and training in the form of a data science degree and working in the industry.

Wrapping up

Data can make all the difference to the functioning of a business. And that underscores the importance of full time or freelance data scientists who can derive insights from vast chunks of big data. This has made data scientist among the hottest job roles in the market right now.

Well then, what are you waiting for? It’s time to start acquiring the skills required for becoming a successful data scientist.

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