Data Science: Technical Skills to become a Data Scientist

Data scientists are in high demand these days, as organizations look to gain insights from the ever-growing flood of data. If you’re looking to get into this exciting field, let’s have a look at the technical skills required to become a data scientist

Data science is an interdisciplinary field that extracts knowledge and insights from structured and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, big data, and a business understanding.

I like to think that Data Science is about combining programming, statistics, machine learning, and AI, computer science, to find interesting insights from the data science project. Then, package it and present it nicely to various colleagues and management within the company, to move from insights to actions.

As data science is a broad and general term (with no clear definition), therefore, the skill set that a data scientist needs is quite broad.

Before we look at some of the requirements and skills that are good to have, remember that a data scientist doesn’t have to be an expert in all these fields, but preferably have profound knowledge and experience in one or two of them, and some basic working knowledge in the others.

A data scientist work with several components (although may not be an expert in all fields) related to:

• Data Engineering
• Mathematics 
• Statistics
• Data visualisations 
• Software Development
• Machine Learning
• Business Understanding

The power of data science is to combine these different areas.

Let’s look closer at the technical skills required to become a data scientist

• Programming
• Mathematics
• Statistics
• Big Data
• AI / Machine Learning / Deep Learning
• Processing large data sets
• Data Visualisation

Languages like Python, R, C/C++ and SQL are the main foundation for most Data Scientists.

Python is one of the most popular data science programming languages data scientists use because of its wide range of uses, such as machine learning, deep learning, and artificial intelligence.

Python can support data collection, modelling, analysis, and visualisation to work with big data and has a large set of available libraries to use.

Read more about Python in our post Python: Introduction to the most Popular Programming Language

R in data science is used to handle, store and analyse data and can be used for data analysis and statistical modelling. R is used in statistical computing and graphics, is easy to learn, and is a free, open-source software.

SQL (Structured Query Language) is one of the most important data science programming languages, as it’s used for performing various operations on the data stored in the databases like updating records, deleting records, creating and modifying tables, views, etc. Data Science is the comprehensive study of data, and to work with data, we need to extract it from the database.

Read more about SQL in our post SQL: The powerful language to extract data from databases

In the post Learn Programming: 7 GREAT Tips to Easily Learn Coding we share 7 tips that will help your learn programming, make your learnings stick better, and have more fun along the way! 

Whether or not you’re learning Python, Java, SQL, or any other programming language, these tips and tricks will help you along the way

A question that might get you excited or nervous is; How much math do you need to become a data scientist?

Well, there is no “one answer fits all” answer to that, as roles and assignments may vary, but in general, there are three topics that consistently come up:

• Calculus: In general, a good knowledge of introductory calculus is important for Data Science algorithms, such as differentiation, integration and multivariate calculus. More about calculus in Data Science and it uses here
• Linear algebra: ​​A component of mathematics that is extremely useful in Data Science and machine learning, as most machine learning models can be represented in matrix form. Linear algebra is used in data preprocessing, transformation, and model evaluation. More about linear algebra in Data Science here
• Statistics: Statistics in Data Science is as necessary as understanding programming languages and is therefore a must-have skill for data scientists. We will look closer at statistics in Data Science in the section below

Statistics is a must-have skill for data scientists as statistics can be considered as the most impactful tool to understand, interpret, evaluate the data. Statistics are at the heart of refined machine learning algorithms in Data Science, capturing and translating data patterns into actionable evidence.

Data scientists use statistics to gather, review, analyse, draw conclusions from data, and apply quantified mathematical models to appropriate variables.

Some of the key concepts and fundamentals of statistics for Data Science

Used to study randomness. It deals with the chance (the likelihood) of an event happening. For example, if you throw a dice, what is the probability that you end up with, say number 4, happening.

In general, probability is evaluated as the probability of an event happening equals the number of ways it can happen divided by total number of outcomes. So for getting a number 4 on the dice is 1/6 probability

A sample is just a part of a population, and sampling methods refer to how we select members from the population to be in our study. The population includes all objects of interest whereas the sample is only a portion of the population. 

There are several reasons why data scientists don’t work with populations, but the main ones are that they are usually very large, and it’s often impossible to get data for every object examined.

There are five types of sampling methods:

1. Random
2. Systematic
3. Convenience
4. Cluster
5. Stratified.

The distribution of data is an essential aspect, with the Normal Distribution, also called Gaussian distribution, as a very significant example (Many real world examples of data are normally distributed).

A distribution in statistics is a function that shows the possible values for a variable and how often they occur. The image below gives examples of a few distributions in statistics; there are a lot, and won’t go into detail in this article, so just to give you an idea what distribution in statistics is

Image Source: Medium Article “Various Data Distributions in Statistics”

Variation is a way to show how data is distributed or spread out and several measures of variation are used in statistics. Some of the key terms to be aware of when it comes to variations in statistics are Variance, Standard Deviation, Range, Error Deviation, Covariance, Correlation, Causality, etc.

We won’t go through all of them, but shortly for two of them, the Standard Deviation measures how spread out numbers are, and the variance measures how far each number in the set is from the mean and thus from every other number in the set.

Hypothesis testing is an act in statistics that is used to test specific predictions, called hypotheses, that arise from theories. The hypotheses testing is used to test the assumption regarding a population parameter and assess the plausibility of that hypothesis by using sample data. 

Four steps in hypothesis testing:

1. Formulate two hypotheses – so that only one can be right
2. Outlines how the data will be evaluated in an analysis plan
3. Carry out the plan and analyse the sample data
4. Analyse the results and either reject the null hypothesis, or come to the conclusion that the null hypothesis is possible

Regression analysis is a way to find trends in data and a way of sorting out which of the variables that do have an impact.

The regression analysis tries to answer questions:

• Which factors matter most?
• Which factors can we ignore?
• How do those factors interact?
• How confident can we be about these factors?

In regression analysis, you have two types of variables:

• Dependent Variables: The main factor that you’re trying to understand or predict
• Independent Variables: The factors you think impact your dependent variable

Therefore, regression analysis is a set of statistical methods used to estimate the relationships between a dependent variable and one or several independent variables.