Cloud Hosting

Times are changing. If you want to be a data scientist in 2023, there are several new skills you should add to your roster, as well as the slew of existing skills you should have already mastered.

Why such an extensive set of skills? Part of the problem is job scope creep. Nobody knows what a data scientist is, or what one should do, least of all your future employer. So anything that has data gets stuck in the data science category for you to deal with.

Youre expected to know how to clean, transform, statistically analyze, visualize, communicate, and predict data. Not only that but new technology (or technology that has recently reached the mainstream) could also be added to your job responsibilities.

In this article, Ill break down the top 19 skills you need to know in 2023 to be a data scientist.

Heres an overview of the ten most important.

These skills will help you land a job, crush an interview, stay ahead of the curve, and negotiate for that promotion. In each section, Ill briefly summarize what each skill is, why it matters, and offer a few places to learn these skills.

While its not 80% of a data scientists job, data cleaning and wrangling are still one of the most important skills a data scientist can master in 2023.

Data cleaning and wrangling are the processes of transforming raw data into a format that can be used for analysis. This involves handling missing values, removing duplicates, dealing with inconsistent data, and formatting the data in a way that makes it ready for analysis.

Cleaning the data usually refers to getting rid of bad/inaccurate values, filling in any blanks, finding duplicates, and otherwise making sure your data set is as spotless and reliably accurate as can be expected. Wrangling it (or munging it, massaging it, or any other weird verb like that) means getting it into an analyzable shape. You convert it or map it into another, easier-to-look-at-format.

Ask any data scientist what they do, and one of the first things they mention will be data cleaning and wrangling. Data never comes into your hands in a nice, clean, analyzable shape, so its super important to know how to get it tidy.

The ability to clean and wrangle data ensures that your analysis results are trustworthy, and helps to avoid incorrect conclusions being drawn.

There are plenty of great options to learn data cleaning and wrangling. Harvard offers a course on EdX. You can also practice on your own by cleaning and wrangling free, raw datasets like the Common Crawl, web crawl data composed of over 50 billion web pages (here), or Brazils weather data (here).

No, its not just a buzzword! Machine learning is a very important skill for any future data scientist to know.

Machine learning is the application of algorithms and statistical models to make predictions and decisions based on data.

Its a subfield of artificial intelligence that enables computers to improve their performance on a specific task by learning from data, without being explicitly programmed. It helps with automation. Youll find it in any industry.

You need to know about machine learning in 2023 because its a rapidly growing field that has become a crucial tool for solving complex problems and making predictions in various industries.

Machine learning algorithms can be used to classify images, recognize speech, do natural language processing, and create recommendation systems. Youll be hard-pressed to find an industry that doesnt do (or doesnt want to) do those ML-assisted tasks.

Being proficient in machine learning allows a data scientist to extract valuable insights from large and complex data sets, and to develop predictive models that can drive better business decisions.

Weve got a repository of over thirty machine-learning projects on ScrataScratch to show this skill off on your resume. TensorFlow also has a set of great free resources to learn machine learning.

This skill is pretty self-explanatory. When you analyze numbers, key stakeholders will want to understand your findings with pretty graphs and charts.

Data visualization is the creation of charts, graphs, and other graphics to help make data easier to understand. You take the numbers youve just cleaned, wrangled, or predicted and you put them into some kind of visual format, either to communicate trends with others or to make trends easier to spot.

In 2023, being able to visualize data is crucial for a data scientist. It's like having a secret superpower for uncovering hidden patterns and trends in the data that might not be obvious at first glance. And the best part? You get to share your findings with others in a way that's both engaging and memorable. As a data scientist, youll work with groups of all different experience levels, but a picture is much more easily understood than a row of numbers.

So, if you want to be a data scientist who can effectively communicate your insights and discoveries, it's important to master the art of data visualization.

Heres a list of free places to learn data viz.

SQL is a Structured Query Language. Data scientists use SQL to work with SQL databases as well as manage databases and perform data storage tasks.

SQL is a very popular language that lets you access and manipulate structured data. It goes hand in hand with database management, which is commonly done in SQL. Database management is basically how you can organize, store, and fetch data from a place. SQL databases are one of the top backend technologies to learn in 2023, so its not just for data science.

As a data scientist, you have to keep track of all the data, make sure it's organized, and retrieve it when someone needs it. Thats what SQL and database management let you do.

Coursera has a ton of great, well-priced database management/admin courses you can try. You can also get a sneak preview of some SQL interview questions here, which can be useful for testing your knowledge.

Big data is a buzzword, yes, but its also a real concept - Oracle defines it as data that contains greater variety, arriving in increasing volumes and with more velocity, or data with the three Vs.

Big data processing is the ability to process, store, and analyze large amounts of data using technologies like Hadoop and Spark.

In 2023, the ability to process big data is critical for data scientists. The volume of data being generated continues to grow at an exponential rate, and being able to handle and analyze this data effectively is essential for making informed decisions and gaining valuable insights. Data scientists who have a deep understanding of big data processing techniques will be able to work with large data sets with ease and make the most out of the information they contain.

Also, thanks to its buzz-wordiness, it never hurts to whack big data on your resume.

I love Simplilearns YouTube tutorial series on this concept.

Cloud computing is the use of cloud-based technologies and platforms like AWS, Azure, or Google Cloud to store and process data. Its kind of like having a virtual storage room that you can access from anywhere at any time. Instead of storing data and computing resources on local machines or servers, cloud computing allows organizations and data scientists to access these resources through the internet.

As I keep highlighting, the amount of data youre expected to work with as a data scientist is growing. More companies will be sticking it in the cloud rather than dealing with it on-prem. It's becoming increasingly important to have the ability to store and process this data in a scalable and efficient manner.

Cloud computing provides an effective solution for this, allowing data scientists to access vast amounts of computing resources and data storage without needing pricy hardware and infrastructure.

The good news is because companies own various clouds, many of them have a vested interest in teaching you about it for free, so you learn to use theirs. Google, Microsoft, and Amazon all have great cloud computing resources.

Wait, didnt we just cover databases? Whats a data warehouse? I hear you ask.

I get you. Sometimes it feels like the most critical data science skill is keeping all the acronyms and jargon straight.

First, lets differentiate data warehouses from databases.

Warehouses store current and historical data for multiple systems, while databases store current data needed to power a project. A database stores the current data required to power an application whereas a data warehouse stores current and historical data for one or more systems in a predefined and fixed schema to analyze the data.

In short, youd use a data warehouse for data for lots of different projects together, whereas a database mostly stores one single projects data.

ETL is a process that involves data warehousing, short for extract, transform, and load. An ETL tool will extract data from any data source systems you want, transform it in the staging area (usually cleaning, manipulating, or munging it), and then load it into a data warehouse.

I feel like Ive repeated this point in every skill, but data is growing. Companies are hungry for it, and theyll expect you to manage it. Knowing how to manage data in buildable pipelines is critical.

Continued here:

Top 19 Skills You Need to Know in 2023 to Be a Data Scientist - KDnuggets