Employment of data scientists is expected to grow 35% from 2022 to 2032. (BLS.gov)
Lets discuss five reasons why most learning methods cause people to fail. Then, I will cover the correct learning method, which worked for me as I transitioned from a history teacher to a machine learning engineer.
Data science is one of the hottest and most lucrative careers right now. With demand for data scientists far exceeding supply, it’s a great time to enter this fast-growing field.
But with so much hype surrounding data science many aspiring practitioners wonder – how do you even get started learning data science?
Luckily, with the wealth of online resources available today you can absolutely begin mastering data science skills on your own before pursuing formal education.
Follow this comprehensive, step-by-step guide to learning data science and you’ll be analyzing data like a pro in no time!
Step 1: Build a Strong Math Foundation
Data science is built on math To analyze and interpret data, you need a solid base in statistics, probability, calculus, and linear algebra.
Brush up on core mathematical concepts like:
- Descriptive and inferential statistics
- Hypothesis testing
- Regression analysis
- Probability distributions
- Multivariate calculus
- Matrices and vectors
Take free online math courses to fill in any knowledge gaps. Having these core math skills will make learning higher level data science much easier.
Step 2: Learn to Code in Python and R
To wield data, you need to be fluent in programming languages like Python and R.
Python is the most popular language for data science thanks to its versatility and user-friendly syntax. Focus on these key aspects:
- Python data structures like lists, tuples, dicts
- Data manipulation with NumPy and Pandas
- Data visualization using Matplotlib and Seaborn
- Machine learning libraries like Scikit-Learn
R is specially designed for statistical analysis and modeling. Master skills like:
- Data wrangling with dplyr, tidyr
- Plotting with ggplot2
- Building models with caret, randomForest
- Creating R Markdown reports
Aim to get hands-on by coding along with Python and R video tutorials. repetition will help cement these coding skills.
Step 3: Learn to Wrangle Data
Raw data is messy and needs to be cleaned and structured before analysis. This process is known as data wrangling.
Get comfortable with essential skills like:
- Importing/exporting data from different sources
- Reshaping, merging, and aggregating datasets
- Handling missing values and outliers
- Parsing dates and manipulating text data
- Using SQL to query and join tables
Practice by wrangling public datasets from resources like Kaggle. The more you work with messy real-world data, the better you’ll get at preparing it for analysis.
Step 4: Master Exploratory Data Analysis
Once you’ve wrangled your data, it’s time to start uncovering insights through exploratory data analysis (EDA).
Key EDA skills include:
- Creating summaries and descriptive statistics
- Visualizing data relationships through charts
- Identifying trends, patterns, and anomalies
- Testing assumptions and transforming features
- Assessing data quality and statistical significance
The more EDA you do, the better feel you’ll develop for working with data hands-on to drive decision making.
Step 5: Build Data Science Models
Now comes the fun part – training models to make predictions from data!
Start by learning basics of essential data science models like:
- Linear and logistic regression
- Decision trees and random forests
- K-means clustering
- Neural networks
- Time series forecasting
Use your new coding skills to train models in R and Python. Determine which models work best for different types of problems.
Step 6: Present Your Findings
A key skill is effectively communicating data insights to stakeholders.
Practice data storytelling and visualization skills like:
- Creating engaging reports and presentations
- Designing insightful charts, graphs, and dashboards
- Conveying findings through clear and concise language
- Tailoring message for audience understanding
Learning to present analyses for maximum impact is a must for aspiring data scientists.
Step 7: Expand Your Toolbox
While Python and R can take you far, it helps to pick up new tools for accessing, storing, and analyzing data.
Consider learning:
- Big data platforms like Hadoop, Spark
- Cloud services like AWS, Google Cloud
- Data engineering tools: SQL, NoSQL, ETL
- Data visualization options: Tableau, Power BI
- Statistical learning frameworks: SciKit Learn, PyTorch, TensorFlow
Having a diverse set of data skills will make you highly employable.
Step 8: Practice on Real Projects
At this point, you should have a solid foundation in data science fundamentals. But book learning will only take you so far.
You need hands-on practice applying your new skills to tackle real world data challenges.
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Find a mentor – Learning from an experienced data scientist will accelerate your growth. They can provide project guidance and constructive feedback.
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Do Kaggle competitions – Tackle complex modeling problems using real datasets from these structured modeling challenges.
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Create a portfolio – Build a portfolio highlighting end-to-end projects to showcase your data science abilities.
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Look for internships – Apply for data science internships to gain practical experience even before finishing your studies.
Nothing develops skills better than working through real data problems.
Step 9: Never Stop Learning
Data science is a fast-paced, ever-evolving field. Lifelong learning is a must to stay relevant.
Make learning a habit by:
- Reading blogs, books, and research papers
- Attending meetups and conferences
- Joining online data science communities
- Taking new courses and specializations
- Starting side projects to try new methods
No matter how much you know, approach each day with the mindset of a student. Valuing continuous learning will ensure a bright data science career.
Start Your Data Science Journey Today
Follow these tips, and you’ll be well on your way to developing in-demand data skills without needing a computer science degree or PhD.
Learning data science takes dedication, but pays off with a fulfilling, lucrative career where you get to solve impactful problems using data every day.
So what are you waiting for? Start building your data superpowers now and see where your data science journey takes you!
Lessons don’t feel relevant
People tend to stick with their studies when they feel like they’re making progress toward their goals. But many data science courses are focused on lectures and drills, rather than doing real-world data science work. This can leave learners feeling like they are treading water.
The right way to learn data science
Before I founded Dataquest, I taught myself the skills required to go from working in a non-technical job to working as a machine learning engineer.
Through that experience — and through the experiences of the hundreds of thousands of learners who’ve gained data science skills on Dataquest over the last eight years — I’ve come to understand a lot about what works and what doesn’t when it comes to learning data science.
And it’s all built into the Dataquest experience.
For learners to be successful, we need to feel like we’re making progress. The importance of this can’t be understated. We need to feel like we can immediately use the skills we’re learning.
That’s why Dataquest is hands-on. You’ll be writing and running real code and working with real datasets from day one.
In our side-by-side learning platform, you’ll read about a concept on the left side of the screen, then be challenged to write and run real code to apply what you’ve learned on the right side.
This simple learning loop repeats through every single one of our courses. You learn something new and apply it to a real data science problem. Each screen builds on the previous screen and leads into the next one. That means that as you’re learning, you’ll know you have grasped the material because you’re using it to do real data science work.
You’re not watching lectures. You’re not filling in the blanks or answering multiple-choice questions. You’re writing and running code exactly like you will in a real data science job.
An important part of this approach is that our courses are carefully sequenced to ensure no gaps. One course always leads to the next one, and each has a very specific goal in mind.
For example, here are some of our career paths:
- 1. Data Scientist
- 35 courses
- 26 projects
- No prerequisites
- 2. Data Analyst
- 25 courses
- 20 projects
- No prerequisites
- 3. Data Engineer
- 19 courses
- 16 projects
- No prerequisites
Many of our students love these paths because they contain everything they need to know to obtain a position. Yeah, really! To become a data analyst, every ounce of information you need is within the Data Analyst career path.
Plus, there are no prerequisites. Anybody can do it!
You can get started for free by clicking on any of the links above.
While all of our courses get you working hands-on with real data, we also know that it’s critical to synthesize the skills as you learn. That’s why most of our courses end with guided projects that challenge you to answer real data science questions using the skills you’ve learned in previous courses.
These projects are fun learning tools that help cement your new knowledge, but they’ll also help you when it’s time for your job search, as you can include them in your project portfolio. (Hiring managers love it when you do this, by the way.)
Example projects in the data science path include:
Data Science Full Course – Learn Data Science in 10 Hours | Data Science For Beginners | Edureka
What skills do you need to become a data scientist?
Apart from the programming language, the other computer science skills you have to learn are: It includes both written and verbal communication. What happens in a data science project is after concluding the analysis, the project has to be communicated to others. Sometimes this may be a report you send to your boss or team at work.
How do I become a data scientist?
You can do this by reading blog posts and articles, watching videos, talking with colleagues in the field, or taking an introductory data science course like Harvard Online’s Data Science Principles. The key is to establish a foundation in data concepts and best practices so that you can take on more complex topics over time.
How do I learn data science?
1. Embrace the Challenge The first step on your journey to learn data science is to confront any mental barriers surrounding your ability to take on the challenge, learn the material, and develop data science skills.
What is the beginner’s guide to data science?
The Beginner’s Guide to Data Science is your gateway to learn the data science mindset from examples. This ebook is written in the engaging and approachable style you are familiar with from Machine Learning Mastery. Discover exactly how to start and what the thought process is in dealing with a data science project.
