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.