Data Scientist Skills to Get You Hired in 2023

Transitioning to a career in data science can mean steady employment in a high-paying industry once you have the right skills.

Each year, there is more demand for data science talent, and with it comes a need for more data scientists to fill the ranks.

Data scientists can create a powerful impact anywhere in any industry, as the application of data science is its own field. It is, however, exceedingly difficult to get a data science job in a competitive market without essential skills and, in most cases, expertise and speciality.

To prepare for a career as a data scientist, start developing a speciality. As you add new skills to your data scientist toolbox, be sure to develop a strong data science portfolio.

If you’re getting started, practicing and developing these skills through the recommended learning resources will help you build up critical skills to make informed decisions.

These skills will help you transcend for a rewarding career in the high-growth field of data science. Let's take a closer what recruiters look for in data scientist candidates, what skills are essential and how you can start learning them.

1. SQL

Data Scientists mostly use SQL to access data from databases to handle structured data.

SQL is one of the most versatile tools that a data scientist can use when working with relational databases.

The easier and critical data science skill that in 2022 you can gain is SQL, even if you have no programming experience. It’s very common for data scientist interviews to include a technical screening with SQL.

Key SQL Skills for Data Science

The SQL skills necessary to be an efficient data scientist include being able to retrieve and work with data.

• Create a database on the local machine and on the cloud
• Ability to explore, query, and extract specific sets of data
• Write complex SQL statements to query the database in Python, R or Scala
• Analyze data using Python or R to gain critical insight (using SQL statements)
• Understanding of modern development and the ability to handle data from multiple sources
• Ability to retrieve data to build reports and perform analysis
• Understanding of string patterns and ranges to query data
• Ability to sort and group data in result sets and by data type
• Ability to organize data efficiently to provide business solutions
• Working knowledge of big data platforms for querying SQL commands

You can develop SQL fluency, even if you have no technical background, with these SQL for Data Science courses from data science educators.

If you learn best from the books, we recommend three SQL books for learning the basic concepts.

• SQL Quick-start Guide: The Simplified Beginner's Guide to Managing, Analyzing, and Manipulating Data with SQL.
• SQL for Data Scientists: A Beginner's Guide for building datasets for analysis.
• SQL Pocket Guide: A Guide to SQL Usage 4th Edition

2. Statistical Programming

A Data Scientist needs strong programming skills in one statistical programming language like Python, R, Scala, etc.

Most organizations prefer statistical programming languages, Python and R, because of its versatility, human-readable syntax, functions, flow control statements as well as libraries and documentation.

Being able to write programs in Python or R means you can clean, analyze, and visualize large data sets more efficiently.

Here’s a list of statistical programming languages for Data Science to choose from:

• Python has t has now become the lingua franca for data science with ocean full of open source libraries/packages for data science and machine learning.
• R language is excellent for complex data analysis, with easy-to-use packages for statistical computing.
• Java provides a host of services when working with data science applications, including big data engineering platforms like hadoop.
• Julia is a high-level programming language excellent for scientific calculations.
• Scala is great for analyzing extensive sets of data with no significant impact on performance and being adopted by data scientists after Python and R.
• MATLAB also makes data science easy with tools to access and preprocess data. You can also build machine learning and predictive models and deploy them.

All programming languages, especially for data science, have a non-negligible learning curve to overcome.

It should be noted that most learners do not complete data science courses, and that is partly because of requisites.

The key to becoming a data scientist is learning and making decisions that support learning progressively. For instance, can you expect a comfortable event if you neglect learning statistics prior to learning statistical libraries in Python?

Make sure you remain committed and focused because most people who want to learn data science—or just brush up on statistical skills—end up paying thousands of dollars with no genuine success.

If you’ve never written code before, we recommend learning Python for Data Science. You can swiftly learn to write programs used to collect, clean, analyze, and visualize data.

Key Programming Skills for Data Science

After you understand the workings of the programs you write, next you need to develop the data skills in programming.

• Reading and Writing CSV Files
• Performing SQL queries
• Web Scraping
• Working with JSON Data (NoSQL Databases)
• Data Exploration
• Data Cleaning
• Data wrangling and preparation
• Data Visualization
• Statistical Data Analysis
• Automate Machine Learning Algorithms
• Build predictive modeling processes

The dominance of Data Science in the world is one reason to choose high-quality data science resources to master the basics and advanced programming concepts and incorporate your skills in context.

3. Mathematical Statistics

The main purpose of statistics in data science is that information is presented accurately in an easy way.

Data scientists are taking over legacy statistician roles and you should consider comfort with statistics before learning statistical analysis.

Statistical Analysis is a form of mathematical analysis that uses quantified models and representations for a set of data or real-life studies.

With a firm foundation in statistics, you’ll be able to:

• Identify patterns and trends in the data
• Avoid biases, logical errors, fallacies
• Produce factual and convincing results

R is great for statistical analysis as it is a programming language for statistical computing while Python also provides a built-in Python library for descriptive statistics and for analyzing larger datasets, NumPy package is excellent for numerical computing and optimized for working with single- and multi-dimensional arrays.

Key Statistics Concepts to learn

Data scientists must understand the fundamental concepts of statistics to perform advanced statistical analysis and predictive analytics on complex data sets.

• Descriptive Statistics
• Understand the Type of Analytics
• Probability Theory
• Central Tendency
• Variability
• Relationship Between Variables
• Probability Distribution
• Hypothesis Testing and Statistical Significance
• Null and Alternative Hypothesis
• Dimensionality Reduction
• Data Sampling
• Over- and Under-Sampling
• Statistical modeling
• Bayesian Statistics

If you're ready to build your statistical skills, explore the resources we've collated so that you can appropriate your knowledge, depending on your level of understanding.

• Basic Statistics concepts for Data Science
• Probability and Statistics Courses for Data Science (Non-programmer)
• Statistics with Python
• Introductory Statistics with R

4. Data Visualization

Garnering insights from data is an essential part of the data science process. Statistical visualization is a key component of being a Data Scientist as you need to communicate effectively.

There are multiple tools like Tableau, Power BI, that provide an intuitive interface, but Python today is the universal language for data visualization in data science community that goes beyond data science, solving real world problems through Machine Learning, Deep Learning, AI, etc.

Data Visualization Skills for Data Science

As a data scientist, you can use data visualization softwares to help present your findings to act on new business opportunities and stay ahead of competitions.

Data visualizations are not as comfortable to create as they look because the skills you need to develop refer to your ability to identify or uncover patterns, correlations and trends, etc.

• Develop audience understanding
• Storytelling with data
• Simple visual design
• Easy to read and understand
• Use clear, concise language to preserve attention
• Empowering and accurate

A data scientist enables the organizations to decide by arming them with quantified insights and data visualization is helps grasp actionable insights.

DataCamp offers several high-quality courses to learn data visualization with Python, R PowerBI, and Tabaleu.

5. Math skills

Data Science involves machine learning and deep learning and it should come as little surprise that the fundamental competencies data scientists need are a core understanding of Linear Algebra and multivariable calculus.

For most data science positions, the only math you need to become intimately familiar with is statistics and probability, but machine learning algorithms, predictive modeling with deep learning and performing analysis or discovering insights from data require sound math skills.

Math Skills for Data Science

Data scientists must have an excellent knowledge of multivariable calculus concepts such as derivatives and gradients, sigmoid functions, step functions, cost functions, min/max values, Rectified Linear Unit functions and function plotting.

The most popular algorithms used by the Data Scientists are:

• Linear Regression
• Logistic Regression
• Decision Trees
• K-nearest neighbour (Supervised Machine Learning)
• K-Means Clustering (Unsupervised Machine Learning)
• Support Vector Machine (SVM)
• Principal Component Analysis (PCA)

Data science requires a firm knowledge of maths and the important data science math skills can be learned. Math is important skills for Data Science, Machine Learning and AI. Learn what basic concepts you need to learn through this math for data science guide.

6. Machine Learning for Data Science

Data Scientists are not required to have an expert level Machine Learning knowledge but a level of familiarity with building algorithms designed to find patterns in data sets, advancing their accuracy over the period.

To excel in Data Science, you must be well versed in programming for machine learning and build skills to work with the advanced machine learning libraries like NumPy, SciPy, Scikit-learn, Pandas, and PyTorch.

FAAANG companies require an expert knowledge Machine Learning knowledge. It's very important to learn the principles of machine learning and the importance of algorithms.

Machine Learning Skills for Data Science

Machine Learning skills for data science are very useful for searching the web, placing ads, stock trading, credit scoring, risk assessments, and for many other applications.

• Build predictive models
• Machine Learning algorithms
• Use data patterns to make informed decisions
• Convolutional Neural Networks Models
• Recurrent Neural Network
• Algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming

The leading responsibility of a data scientist is to provide solutions using machine learning models to solve complex business problems.

Python is a brilliant choice for Machine Learning. It has powerful libraries like NumPy, Scipy, Scikit-learn, Pandas, and PyTorc for creating ML Models.

Learn Machine Learning for Data Science: Get an overview of the modern data ecosystem with machine learning resources.

7. Deep Learning and TensorFlow

Deep Learning has become an important element of data science and TensorFlow is heavily used by data scientists for research and high-level implementation of ML algorithms.

Deep Learning is very hard and TensorFlow is also difficult to learn and even difficult to use too, but the working knowledge of Deep Learning Algorithms and Frameworks are the hottest scientific skills.

Deep Learning skills help solve the most complex business problems and to excel as a Data Scientist, you must consider upskilling yourself so that you can become proficient in using PyTorch and TensorFlow.

Deep Learning has silently revolutionized the world and you must build familiarity with classification, recognition, perception, discovering, prediction, and Creation, etc.

It's difficult to get an entry-level Data Science job, but Deep Learning skills will put you in the league of the most skilled Data Scientists.

• Discrete mathematics
• Neural Network Architecture
• Data Modelling and evaluation
• Natural Language Processing
• Deep Reinforcement Learning
• Distributed Deep Learning Systems

If you join an online course or a bootcamp, you will gain skills that will help improve your proficiency as a Data Scientist.

You can get an overview of the deep learning with TensorFlow. TensorFlow is an end-to-end open-source platform for machine learning and deep learning. It provides a collection of workflows to develop and train models.

Learn to train models using Python with of these TensorFlow Courses from world-class educators.

Five Technical Skills that you need for a high-paying Data Science Job

• Unix — It has become imperative that Data Scientists must know about Unix and Linux systems.
• Big Data Frameworks — The next skill that will help you fetch a high-paying Data Science learning job is having the working knowledge of Apache Spark and Apache Hadoop.
• Distributed Computing — As Data Scientist, you'll work on a large volume of data, and it is imperative to have some knowledge about distributed computing.
• Data Modeling & Model Validation — Data modeling techniques are also used in Data Science to identify valid patterns & classifications on datasets.
• Software Development — This skill may seem unnecessary but Data Scientists should posses basic knowledge of system design and application deployments to collaborate with Cloud Engineers, Data Engineers, Machine Learning Engineers, and Ai Developers.

Tips for Learning data science skills

Data Scientists leverage these skills to share their findings with key stakeholders and enable data-driven decisions at their organizations. Putting in the time and effort to learn data science skills can set you up for a rewarding career as a Data Scientist.

If you are just starting out in data science, there are some concrete steps you can take to improve your chances of landing an entry-level data scientist job include.

Here are a few quick tips for getting started:

• Work on developing programming skills, either through online courses or books
• Set aside time to daily practice programming
• Learn from your mistakes
• Practice with real data projects
• Build a portfolio comprising either self-directed or group projects
• Join an online data communities
• Build your skills step-by-step
• Gain experience through an internship or open-source collaboration opportunity

CONCLUSION

Every company, every business, requires Data Scientists. It is profitable for you if you have the aforementioned skills to impress the recruiters.

There are endless opportunities in Data Science, and it is a rewarding career. In this article, we have discussed the Indispensable data scientist skills to get hired in 2023.