Data Science Roadmap - A Comprehensive Career Guide
This blog post will provide you with a comprehensive data science roadmap that can aid your learning, helping you succeed in a world loaded with data.
As of 2020, the average salary that a data
scientist makes in the US is over
$113,000. With that stated, it can be affirmed
that data scientists are high in demand. You can think of data science as a way
to earn money but then you will never have the actual motivation to learn it.
Instead, you should identify a problem; be it marketing-related or a
research problem, and then start learning data science & its tools
accordingly, because you cannot excel at every tool or a data science skill set.
First & foremost, you need to motivate yourself to
love the data, with no drive you will probably leave your learning journey at
some point. Furthermore, you need to work on real projects. Just
acquiring the fundamental skills won’t make you an expert, likewise, to
increase your expertise, you need to increase the level of
difficulty every time you undertake a data science project. While being at work
or at an internship, learn from your peers & subordinates, check how they are
executing the data science projects. Last but not least, present your
insights & analysis to others.
But you might be wondering what skills do you exactly
require for being a successful data scientist & how to start? What steps do you need to follow to leap into the field of data
Before we get started with the actual data science roadmap, which of the following expertise/skills do you have?
Since now you have a know-how of what skills you
already possess, the roadmap below can help you understand where
you stand & what effort is needed for you to reach the
Step 1: Getting Started
Before you move on to learning & adapting to new
skills, it is important for you to understand what data science is & whether you
are a great fit for data science or not.
This article by innoarchitech precisely explains what data science is, it further enlightens on the roles of data scientists, data engineers, and data analysts that can surely help you in deciding which boat to jump in.
To further assess, check what type of data scientist you are with the below short quiz:
Step 2: Learn the basics of mathematics & statistics
The next checkpoint in the data science roadmap is to learn the fundamentals of mathematics & statistics. The topics listed below should be your area of focus:
- Descriptive Statistics
- Inferential Statistics
- Linear Algebra
- Structured Thinking
You can further enrich your concepts with these 5 free statistics books, along with these amazing resources to learn math for data science. If you are wondering why math is needed, then you need to do a quick browse at this blog post by Dave Langer from Data Science Dojo that explains why math is important in data science.
Step 3: Acquainting the Key Tools for Data Science
1. Python: It is one of the most popular
& widely used programming languages. Learning this language can help you
with creating web applications, handling big data, rapid prototyping, and much
more. To know more about python, check this introductory
blog post for
Learn all the fundamentals of Python for Data Science with our upcoming training!
2. R: Another popular language for programming in R. It provides a free software environment for statistical computing. These few blog posts can definitely add value to your knowledge of R programming:
- Logistics Regression in R
- R language programming for Excel Users
- Natural language Processing with R programming books
You might be stuck with the same traditional argument between R Versus Python; if you are wondering which one of them you should opt for, then I did suggest you begin with R and transition to Python gradually. Then use them as per the needs of your organization.
3. Data Exploration & Visualization: If
you are into the analytical side of the data i.e. data analysis then you must
learn data exploration & visualization. Data exploration being the initial
step of data analysis, while, data visualization is the graphical
representation of data itself. Both Python & R can
be used for exploring & visualizing the data.
Step 4: Learning the Key Tools for ML
There exist some basic and advanced machine learning
tools that you need to learn & adapt yourself with. Some of the most important ones are listed below. These skills can be of immense value in your overall data science roadmap:
- Exploratory Data Analysis & Data Cleaning: Before moving on to the ML tools, you need to be well versed with what EDA & data cleaning is. EDA or exploratory data analysis, is a way of studying the datasets to summarize them into a visual format. Data cleaning is the process of detecting & correcting errors, and ensuring that the data is free of errors.
The below cheat sheet & the article here can help you get started with EDA now.
2. Feature Selection & Engineering: This should typically be your next step in learning ML. This uses domain knowledge to obtain the features from the data, which in turn helps with improving the performance of ML algorithms. So, if you are willing to gain expertise in the ML domain, you need to learn about feature selection & engineering.
3. Model Selection:
Out of all the statistical models, you will need to select one model that is
well-suited for your problem. These are some of the statistical models that you
can go with:
Regression:It is an algorithm of supervised machine learning, where the slope
is constant & the predicted output is continuous. To get started with
linear regression, check out this comprehensive
cheat sheet by MIT.
Regression:It is an algorithm for supervised learning classification that is
used to predict the probability of a target variable. It is typically used for
classification purposes. This article can be a great resource for you to get started
with logistic regression in R.
Trees:This generally uses a decision tree to form assumptions &
conclusions about the target values. It is one of the most common approaches of
predictive modeling used in statistics & machine learning.
To build your understanding of a decision tree,
this comprehensive tutorial can be of great help to you.
Neighbor (KNN): It is one of the most simple supervised machine learning algorithms
that can help with resolving regression & classification problems. It is
quite easy to comprehend and learn. But has a few drawbacks.
E. K-Means:This is an unsupervised learning algorithm that units the unlabelled sets into
diverse clusters. Where K represents the numeral of the centroid. This cheat sheet from Stanford university can help you with learning about
F. Naïve Bayes:It is one of the algorithms for supervised learning that helps in solving classification problems. It is
considered one of the most successful algorithmsbecause of its nature to create fast
ML models can help with making predictions. Here you can find more about Naïve
Reduction: A process of transforming the high-dimension space to a
low-dimension space to maintain the meaningful properties of data.
Learning dimensionality reduction is an important skill that every data scientist must possess. Break the curse of dimensionality with Python.
H. Random Forests: It is an ensemble learning method for
classification, regression, and other task purposes. It includes drawing
multiple decision trees at a time & outputting the class that is the mode
of all. Dive deep with this amazing guide by Berkley University.
Boosting Machines: One of the most leading techniques to build predictive
models. It helps to deal with regression & classification problems and
creates a prediction model in the form of an ensemble of the weak prediction
This guide can help you get started with Gradient Boosting
J. XGBOOST: This tool specifically helps with executing
the gradient boosted decision trees devised for speed and performance.
Find answers to what is XGBOOST, how to build an intuition
for it, and much more with the guide here.
K. Support Vector Machines: These are supervised learning
models that are coupled with associated learning, they aid in evaluating the
data for regression & classification analysis.
The below graphic by Avik
be a great help for you to get started with SVMs:
4. Model Evaluation: Moving towards the last step of machine learning, model evaluation, it generalizes the accuracy of the model based on the future data. It typically uses two methods, holdout &
Step 5: Profile Building
Building a profile on GitHub is an important task that every data scientist must complete. It is one of the most effective ways for a data scientist to gather all the code of the projects they have undertaken. It showcases your code and projects undertaken and shows how long you have been practicing data science.
To get started check this cheat sheet on GitHub.
Moving on, you need to be part of some discussion forums. These will help you find an answer to the questions you are stuck at. Here are some of the discussion forums you can be part of:To gain more knowledge in the data science domain, start following different YouTube channels.
Our YouTube channel can surely be a good start for you.
Step 6: Prepare for Data Science InterviewYou need to know all those key data science concepts that can help you ace your interviews. With these 101 Data Science Interview Questions. Answers, and Key Concepts you can prep up yourself for the interviews.
Step 7: Take A Look at Typical Data Scientist’s JobReaching the end of your data science roadmap, you might want to get an idea of a typical data scientist’s job. It is always helpful to look at some job descriptions, showcase your skills, and stand out as the best candidate. If you think you are a good fit for it, you must get started right away!
Before I end this post, let me repeat it again, instead of trying to learn all the skills required to be a data scientist endlessly, pick up a problem that inspires you or bees relevant to your domain. Try to solve that problem using the data science skills, only pick up the skills necessary to solve that problem. As you solve more problems, you will learn more skills along the way.
If you hated probability in high school or university, it is because every example of probability has to do with coin tosses and dice. But if you happen to come across interesting problems, such as the Birthday Paradox, you might have ended up loving probability.
B. Best Places to Work as a Data Scientist Around the World
So, what have you decided? Are planning to get started with Data Science? Take a look at our Data Science Bootcamp, a great way to start your data science journey.