Do you want to learn data science? Whether you’d like to land a job as a data scientist or further your career by learning new skills, this article covers how to learn data science in 2023.
It’s no secret that data science remains an essential tool for forward-thinking organizations that want to unlock hidden value from their ever-growing data.
And when you factor in that the Bureau of Labor Statistics reports an average salary in excess of $100,000 for data scientists, taking the time to learn data science can be highly lucrative.
So if you want to learn data science, you’re in the right place! Let’s dive in to help you learn the skills you need to enter the data science job market.
Why Should You Learn Data Science?
Data is the king of business now. A huge amount of data is generated by companies and customers every second, and using this data, companies can gain a lot of information about their customers. This helps them make better business decisions and achieve a better hold in the industry.
There are applications of data science in every field, be it finance for fraud detection, banking for enabling more secure transactions, healthcare, retail, logistics, supply chain management, and many more. Learning data science gives you a wide range of career opportunities for life. You can explore various domains and skills and specialize in multiple areas as well.
Prerequisites to Learn Data Science
In 2023, most data science courses and tutorials aim to teach you from scratch, so you can expect to learn computer science fundamentals, data structures, algorithms, statistics, math, and of course, programming languages like Python, R, and SQL.
That said, it does help if you already have knowledge in the following areas:
- Basic math concepts like differentiation, integration, linear algebra, etc., will undoubtedly help.
- Same way, knowledge of basic statistical measures like median, mean, etc., and probability will become essential as you take up more advanced courses.
- Knowing at least one programming language, concepts of OOP programming, and data structures will be helpful.
Choosing an Integrated Development Environment (IDE)
How to learn data science depends on how you practice, and an IDE is one of the best ways to do this, as they provide lots of helpful features to let you focus on the important stuff.
It is also easier to work with IDE when importing libraries, setting up an environment, and compiling code. Some of the top IDEs you can consider are:
- Jupyter for Python: Although there are many excellent IDEs for Python, Jupyter is one of our favorites because it is easy to set up and use. It is light as it is based on a web application based on the client-server architecture. You can instantly get started and play around with code to create visualizations and presentations.
- RStudio for R: For those who prefer using R for data science, RStudio gives a rich experience. It has an open-source version as well as a commercial edition. RStudio provides rich graphics and code completions features along with syntax highlighting and smart indentation. RStudio also provides exhaustive documentation and help functions for developers.
- Scala IDE for Eclipse: Eclipse is a popular IDE for Java and has a similar version for Scala too. You can develop pure Scala and Scala-Java mixed applications and add references from Java to Scala and vice versa. It is fast and catches compilation issues as you write. Eclipse also has a smart indenter that formats the code, provides highlighting support, includes comments, code folding, and more.
- Online IDE: One of the most popular online platforms is Google Colab, which is built on top of Jupyter and runs on Google Cloud Platform. It supports Python 2 and 3. You can learn to code both machine learning and deep learning algorithms and work with advanced libraries like Keras, OpenCV, TensorFlow, etc. It is free and can be accessed via a browser.
How To Learn Data Science
Data Science requires different types of skills, so you'll need to cover multiple learning areas. If you want to specialize in a particular area or job, for example, you want to be a data engineer, you should focus on those specific skills (like SQL), but for a data scientist, broader knowledge is essential.
For example, you may not need to code algorithms, but you should know the logic behind it. Equally, you may not be involved in plotting the graphs and charts, but you should know how to infer and analyze the visualizations to get the most out of the data.
In general, you'll need to know the following phases of data science:
- Data discovery and collection/acquisition
- Data cleaning and transformation
- Exploratory Data Analysis
- Machine learning techniques
- Evaluate and improve results
The Best Data Science Courses
One of the best ways to learn data science is to take one of the best online data science courses. And while no single course is complete, we've listed 5 of our top picks for beginners to get started with their data science journey:
A-Z is a paid beginner course from Udemy and covers data modeling, mining, and visualization using real-world examples. The course covers statistical and machine learning techniques like linear regressions, logistic regression, Chi-square test, confusion matrix, etc. You will also learn to use Tableau for visualization and SSIS for database interaction. Very practical and hands-on in its approach.
A 10-course specialization that takes about 11 months with about 7 hours per week (you can set your own timelines!) is for beginners to launch their data science career. The specialization has a rating of 4.5/5 from over 80000 reviews. You will learn R programming, data collection, cleaning, EDA, statistical inference, regression models, machine learning, and creating data products to automate complex tasks.
Udacity’s Data Science Nanodegree program provides a hands-on approach to learning data science. This program will help you master topics such as natural language processing (NLP), running pipelines, transforming data, building models, designing experiments, and deployment.
Codecademy’s data science foundations course begins by teaching you the principles of data literacy before moving on to topics like the fundamentals of statistics for data science and communicating data science findings. You'll also learn about exploratory data analysis (EDA) techniques and data wrangling, culminating with lessons on popular Python tools like Pandas and Matplotlib.
With this data science bundle, you get 7 separate courses, allowing you to curate your own data science learning journey. If you want to learn Python, you get access to several courses that focus on the fundamentals of Python and R, including lessons on NumPy, Pandas, and Maptlotlib.
Alternatively, there’s a comprehensive and practical 22-hour course on the practical side of data cleaning, processing, wrangling, manipulation, and visualization with R. You'll also learn to use the Streamlit library to create data science apps, cover applied probability and statistics and also dive into Deep Learning with Keras.
Build Data Science Projects
Another great way to learn data science is to build data science projects. You can start with simple projects based on linear regression, k-means clustering, decision tree, or Apriori algorithm, which are relatively simple.
For example, you can perform market basket analysis, take up the customer segmentation project, or collect information to determine whether a person is likely to take an insurance policy or not. The options are endless, and we highly recommend this approach.
Pursue Data Science Certifications
When you've learned data science skills, and you want to demonstrate these in a verified way to potential employers, you should definitely consider data science certifications. These can be an excellent way to get an edge over other data scientists by enhancing your resume and giving you more exposure through challenging real-world projects.
Prepare For Data Science Interview Questions
When you step out into the data science job market, you will learn that data science is vast and that there are many potential data science interview questions from each phase of the data science lifecycle.
In general, you should know about machine learning and its types, a little bit about algorithms (or more if you are experienced), deep learning, tools, and techniques for data science like TensorFlow, Tableau, SQL, Python/R/Java, or any programming language that you have used for Data science.
Here are some typical questions asked in most data science interviews:
- Can you enumerate the various differences between Supervised and Unsupervised Learning?
- Could you draw a comparison between overfitting and underfitting?
- Please explain the role of data cleaning in data analysis.
- Please explain Eigenvectors and Eigenvalues.
- What are outlier values and how do you treat them?
- What do you understand by Deep Learning?
- What are the skills required as a Data Scientist that could help in using Python for data analysis purposes?
- What is an Activation function?
- What are the different steps in LSTM?
- What are hyperparameters?
Roles & Responsibilities of a Data Scientist
When the time comes, and you're ready to land a job as a data scientist, here are some of the key roles and responsibilities you should be prepared for:
- Work with stakeholders to understand the problem(s) faced by the business
- Give a proper definition and structure to the problem and collect data according to that
- Assess the accuracy and relevancy of data, clean and transform the data into a usable form
- Create visualizations to perform an initial analysis of the data and find patterns
- Build algorithms and data models and evaluate the accuracy of models
- Use the insights from the model to make business decisions and improve overall customer experience, thus increasing revenue and market share
- Monitor and evaluate results and work on the feedback based on the results
With that said, maybe you're curious about the difference between a data scientist and a data analyst, or perhaps you're interested in other roles that are in the modern data team. Let's take a look at these now:
- Data Scientist: These design data models and create algorithms for predictive models by performing detailed data analysis. They also communicate with stakeholders and develop new user stories from solutions.
- Data Analysts: These use tools and techniques to transform and manipulate huge data sets, find trends and patterns, and generate useful insights and conclusions, leading to better business decisions.
- Data Engineers: These get raw data from multiple sources, then clean, sort, and process data to make it usable for further analysis.
- Data architects: In this role, you need to plan, design, create, and manage the data architecture of an organization
Future Prospects for Data Science
The digital world is picking up quickly, and data science will remain a crucial part of business expansion for years to go. With the popularity of AI, Natural Language Processing, and other related technologies, data science will find more demand in more domains than it already has, thus generating more job opportunities in data science and related fields.
Programmers, DB admins, big data engineers, software architects, and data analysts will be in high demand, along with data scientists who will have a significant role to play in managing all the above resources as well as performing their own tasks.
So there you have it, if you want to learn data science in 2023, you now have all of the information you need, including some data science courses that you can use to kick-start your learning journey.
Whether you’re just starting out in your data science career or want to level up your existing skills, we hope the tips we’ve included in this article help you achieve your data science career goals.
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