The Ultimate Data Science Roadmap to Make You an Expert Data Scientist

Data Science Roadmap

9 steps in the Data Science Roadmap to take you from a beginner to an expert

With the world going digital, companies are starting to understand the importance of data science and are adopting its applications in their daily workflow. Artificial Intelligence and Machine Learning are almost in some form or the other. As technology develops further, the already booming field of data science will reach unforeseen heights, making it one of the best and highest-paid careers to step into at present. Interested, but don’t know where to get started? We’ve got you covered. We tried our best to compile an exhaustive guide in form of a data science roadmap to follow to go from an absolute newbie to an expert .

STEP 1: Fundamentals of Artificial Intelligence

Be it something as complicated as Sophia the robot or self-driving cars, or something as simple as spam filters in your mailbox or Netflix’s recommendations, Artificial Intelligence, or AI, is everywhere. But what is artificial intelligence? In layman terms, it is man-made or simulated intelligence. It is a branch of computer science that deals with making smart machines that can “think” and make decisions on their own.

AI research and development are booming because the introduction of robots and automated machines reduce the human effort required and the probability of error. They reduce the expense of industry and increase the revenue, making them a highly anticipated commodity.

Self-driving cars will soon be available in the market, and factories will be almost entirely automated. Decades down the line, AI will be as much a part of society as we humans.

To gain a better understanding of the subject, you can go with the following course on Introduction to Artificial Intelligence by IBM.

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$39.00
  • Coursera
  • IBM
  • Online Course
  • Self-paced
  • Beginner
  • Less Than 24 Hours
  • Free Trial (Paid Course & Certificate)
  • English
  • None Pre-requisite
  • Artificial intelligence Data Science Deep learning Machine learning
Learning Experience
9.2
Content Rating
9.6
PROS:
  • Easy to understand the basic concepts in Artificial Intelligence
  • Explains the most basic ideas, terminologies, and technologies behind AI
  • Tests and assignments are good to test your understanding of the subject.
CONS:
  • Would be great if a more detailed view of the IBM Cloud platform was given.

This course in the Data Science Roadmap provides the learner with an overall sense of the concept of Artificial Intelligence. Starting with what AI is, the course will take you through some applications and real-world examples of AI, proceeding to explain some concepts and terminology used in the field.

It will also familiarize you with some concerns and issues regarding AI, which will help you innovate and make decisions during the implementation of an AI solution. You will also get to know the current opinion on the future of AI and get experts’ advice regarding a career in the field. A demonstration of AI in action by utilizing Computer Vision to classify images will wrap up the course.

PROGRESS

5% progress

STEP 2: Mathematics for ML: Linear Algebra

Now that you are familiar with the concept of AI, you are ready to get started with the basics of what goes on behind the scenes. Before getting into the more technical aspects, it is vital that you get comfortable with the math that makes everything work – Linear Algebra. Having a clear understanding of Linear Algebra will help you understand your models much better and deal with problems that you face in an efficient way.

Linear algebra is the branch of mathematics that deals with linear equations and their representation in vector spaces, matrices, and their transformations. It is one of the fundamental components of data science algorithms.

For developing ML models, one needs to have a careful understanding of the fundamentals of linear algebra. Following course in the Data Science Roadmap on  Mathematics for Machine Learning by Imperial College of London does an excellent job of the same.

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$49.00
  • Coursera
  • Imperial College London
  • Online Course
  • Self-paced
  • Beginner
  • Less Than 24 Hours
  • Free Trial (Paid Course & Certificate)
  • English
  • Basic Maths
  • Eigenvalues And Eigenvectors Linear Algebra Essentials Machine learning Transformation Matrix
Learning experience
9.4
Content Rating
9.2
PROS:
  • Great way to learn Applied Linear Algebra
  • Should be fairly easy if you have any background with linear algebra
  • The team of lecturers is very likable and enthusiastic
CONS:
  • Requires fundamental understanding of Linear Algebra
  • Learner recommended intermediate level for enrollment to this course
  • Issues in the auto-grading of python notebooks in week 3

The course is hyper-focused on developing your mathematical intuition and not on making you do long calculations using your pen and paper. You start off with an introduction to vectors and some basic operations done on vectors, like the dot product, cross product, etc. After getting you comfortable with vectors, the course moves on to teach you objects that operate on vectors – matrices.

This course in the Data Science Roadmap will take you right from the absolute basics of matrices to more advanced operations and concepts like eigenvalues, eigenvectors, etc., making it an ideal choice for people who are utterly new to linear algebra.

PROGRESS

10% progress

STEP 3: Computer Programming

Now that you are comfortable with the mathematics behind the scenes, you are ready to begin implementing. To implement these algorithms, you first need to have a strong command over computer programming fundamentals and the programming languages that are widely used in data science.

All programming languages run on functions (sometimes called ‘procedures’ or ‘methods’). Functions are, in layman terms, collections of code to accomplish a specific task.

These functions act on chunks of data stored in the machine, called files, typically aggregated in databases for easier access, management and updating. The task to be accomplished depends on the actual code. Some functions may be written for data collection. At the same time, another may process this data to be suitable for use for giving the desired output.

These functions, in object-oriented programming, are implemented using classes. Many complex goals can be accomplished by making multiple classes interact with each other. One such form of interaction is inheritance, where one class inherits the properties and methods of another parent class.

The most widely used programming languages in data science are Python and R, with Python being ideal for beginners owing to its intuitive syntax. Thanks to this specialization by the University of Michigan, one can gain a detailed understanding of the aforementioned programming concepts, simultaneously employing them in Python3 over a span of 4 courses.

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$49.00
  • Coursera
  • University of Michigan
  • Microdegree
  • Self-paced
  • Beginner
  • 3+ Months
  • Free Trial (Paid Course & Certificate)
  • English
  • Python
  • None Pre-requisite
  • Data Science with 'Python' Python Programming
Learning experience
9.2
Content Rating
9.2
PROS:
  • Very comprehensive and easy to understand
  • Beneficial course on object-oriented programming in python 3
  • Functional project at the end helps to understand how recommendation systems work
  • Hands-on practice with the Runestone Notebook Environment
CONS:
  • Little bit challenging, requires completing of Course 1
  • Runestone project needs debugging

You might also want to start using Jupyter Notebooks, which are basically digital notebooks where you can not only write live codes but also include text and equations. These notebooks help you organize your code and share it with others.

You might also want to check out RStudio, an open-source development environment for R that also provides data science tools.

PROGRESS

30% progress

STEP 4: Statistics with Python/R

Once you are comfortable with a programming language, the next step in the Data Science Roadmap would be to learn to implement statistics in it. Like linear algebra, statistics is another fundamental component of data science.

A go-to resource for extensive learning of statistics, in my opinion, the  Statistics with Python Specialization specialization by the University of Michigan, which walks you through all the statistics knowledge you need for data science using Python.

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$49.00
  • Coursera
  • University of Michigan
  • Microdegree
  • Self-paced
  • Beginner
  • 1-3 Months
  • Free Trial (Paid Course & Certificate)
  • English
  • Python
  • High School-level Algebra
  • Data Analysis Data Science with 'Python' Data Visualization Practical Statistics
Learning experience
9.2
Content Rating
9.1
PROS:
  • Excellent course content, thoughtfully composed and carefully edited
  • Helpful course for a newcomer in data science studies
  • Supplementary material in Jupyter notebooks is extremely valuable
  • A great introduction to regression and bayesian analysis in python
CONS:
  • Python coding instruction itself could have been more detailed
  • Codes can be refactored in a way that can be more suitable for reproducible studies

This specialization comprises three courses:

The first course introduces the learner to the field of statistics. You will learn where the data comes from, data design, data management, data exploration, and data visualization. After completing this course, you will be able to identify the different types of data and analyze and interpret summaries of both univariate and bivariate data. It will also familiarize you with the concept of sampling.

The second course builds upon this by teaching you the basic principles behind the usage of data for estimation and assessment of theories. You will be introduced to construct confidence intervals and use sample data to verify if a theory is consistent with the data. A heavy weightage will be given to interpreting inferential results appropriately.

The final courses will make you employ the knowledge you have gained in the previous courses to construct statistical models in such a way that they fit the data. Statistical inference, which you learned in the last course, will be used to emphasize the need to connect research questions to data analysis methods.

After having completed this specialization, you will be equipped with statistical modeling techniques like linear and logistic regression and generalized linear models, Bayesian inference techniques, etc., which will be a crucial component of machine learning, and data science in general.

Every course in this specialization has a lab component, which will allow you to employ what you have learned immediately.

PROGRESS

40% progress

STEP 5: Data Visualization

Data visualization is not only vital for data science but is also highly valued in the corporate world because visual representation is much more effective and easier to understand as compared to, say, a table of numbers or a few paragraphs. It makes conveying your points and persuading others effortlessly.

To be an expert in the field, one must be well-versed in both data analysis approaches, namely, qualitative data analysis and quantitative data analysis.

You need to be familiar with various charts, viz bar charts, pie charts, waterfall charts, area plots, box plots, etc. You should also be able to perform Exploratory Data Analysis to draw conclusions from the data provided to you.

For data visualization, our team of experts has curated a list of courses which can be found in the following post on our website. Following those courses will make you a Data Vizard for sure.

courses in data visualization

Best Courses in Data Visualization to Transform your Data Science Journey

Are you looking to get hands-on data visualization? We have curated a series of top courses in data visualization for you to get started. Data visualization is considered one of the powerful tools in data analytics. It plays a vital role by converting the massive amount of intangible data into pictures and graphics that can […]

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PROGRESS

50% progress

STEP 6: Machine Learning

Now that you are well-equipped with all the basics of data science, you are ready to do some real “big boy stuff.”

All of the skills you have acquired up to this point can be employed to develop algorithms that can learn from experience and gradually improve their accuracy of performing tasks like predicting outputs etc.

The most common application of ML models is a recommendation system, be it Netflix or Amazon Prime.

You need to familiarize yourself with Google Colab, a cloud-based variation of Jupyter Notebooks, to implement your ML models. Google Colab comes with a cloud-based GPU which massively reduces the time required to train models. The following course on Machine Learning with Google Colabs will help you get comfortable with Google Colab.

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$19.99
  • Udemy
  • Online Course
  • Self-paced
  • Beginner
  • Less Than 24 Hours
  • Paid Course (Paid certificate)
  • English
  • Python
  • Google Colab
  • None Pre-requisite
  • Machine learning
Learning Experience
9.2
PROS:
  • Very well explained concepts
  • Sessions are good and informative

Before some data can be used to build ML models, it needs to be processed in order to remove duplicates and deal with blanks. This is called data cleaning, which is crucial to ensure the maximum accuracy of the model. The course on Data Cleaning in Python will do a great job of guiding you through the process.

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8.2/10

Data Cleaning in Python

  • Udemy
  • Online Course
  • Self-paced
  • Beginner
  • Less Than 24 Hours
  • Paid Course (Paid certificate)
  • English
  • Python
  • Jupyter Notebook
  • Basic Scripting in Python
  • Data Cleaning Python Programming
Learning Experience
8.2
PROS:
  • Well designed course content
  • Every concept is explained very well
  • Programming aspects of each concept can be understood by anyone who has basic knowledge of Python.
CONS:
  • The example code is mostly list based instead of data frame-based

Now that you have prepared your data, you are ready to get started with building an almost intelligent algorithm. The general way to go ahead with this is splitting your data into two parts: train and test.

The ‘train’ segment is used to train the machine, the process of which is broadly categorized into three types:

1. Unsupervised Learning

The input data in this training method is unlabelled and unclassified, which means that neither do they have associated outputs nor are they grouped. It is upon the algorithm to classify the data points and label them without any external guidance. Clustering and Anomaly Detection are some unsupervised learning approaches.

2. Semi-supervised Learning

This method is based on combining a small amount of labeled data with a large amount of unlabelled data during training

3. Supervised Learning

Machines are trained using well-labeled data, which means input data is tagged with its associated output. Models like regression models and classification models are some supervised learning models.

For training the model, you should be capable of selecting the ideal features. Once the model is trained, you need to optimize and evaluate it, and then analyze the results. For becoming an expert in ML, the following course offered by Stanford University is a course you will see being recommended everywhere. This course also deals with Neural Networks, which is a subset of ML concerned with allowing the model to recognize patterns and solve problems on its own.

PROGRESS

70% progress

STEP 7: Text Mining and Analysis

Text mining refers to transforming unstructured data into structured data to simplify its analysis. This is done using NLP (Natural Language Processing). Today, text mining and analysis are widely used for risk management, customer service, fraud detection, business analysis, and many other sectors.

The following course by the University of Illinois is the place to go to if you want to learn text mining and analysis.

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$79.00
9.1/10

Text Mining and Analytics

  • Coursera
  • University of Illinois
  • Online Course
  • Self-paced
  • Beginner
  • 1-4 Weeks
  • Free Trial (Paid Course & Certificate)
  • English
  • None Pre-requisite
  • Data Mining Natural language processing Probability
Learning Experience
9.0
Content Rating
9.2
PROS:
  • The pipeline proposed helps you understand text mining
  • Well defined key concepts and techniques for text mining and analytics
  • Provides a good foundation of text mining and analytics like PLSA and LDA
CONS:
  • Difficult programming assignments and quizzes

This course in the Data Science Roadmap will go into detail about what is text mining and will teach you text clustering and categorization. It will also present you with an overview of the various NLP techniques used for it. For the computer to perform actions on language inputs, the language must be converted into numbers because that is what a machine understands. The device then identifies patterns and performs operations. This is called text representation, one of the significant NLP tasks that the course covers. Another NLP task that you will be taught is generating word clouds, which you can then use for word association mining.

You will also learn advanced concepts like Paradigmatic relations, Topic Mining and Analysis, Mixture Models, Expectation-Maximization (EM) Algorithms, Probabilistic Latent Semantic Analysis (PLSA) Latent Dirichlet Allocation, Sentiment analysis, Opinion Mining, Latent Aspect Rating Analysis, and Contextual Text Mining.

Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation, Sentiment analysis, Opinion Mining, Latent Aspect Rating Analysis, and Contextual Text Mining.

PROGRESS

80% progress

STEP 8: Deep Learning

At this stage of the Data Science Roadmap, the learner will be ready to dive into a more advanced application of ML – Deep Learning.

DL algorithms are built around Neural Networks, structures that try to mimic the way the neurons of humans work. Layers and layers of these neural networks form the “brain,” termed deep learning.

Deep learning is very deeply integrated into the services we use in our daily lives. Financial institutions use DL algorithms for algorithm trading, risk assessment, fraud detection, etc. In contrast, lawyers use it for speech recognition and to enhance the analysis of evidence. DL has found widespread applications in other fields as well, making it a skill in high demand.

Despite shortcomings like the opacity of NNs, the requirement for loads of high-quality data, etc., the commercial market for DL models is increasing. To catch up to the industry, you can refer to the below specialization by deeplearning.ai (Andrew Ng).

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  • Coursera
  • deeplearning.ai
  • Microdegree
  • Self-paced
  • Intermediate
  • 3+ Months
  • Free Trial (Paid Course & Certificate)
  • English
  • Python
  • Intermediate Python Skills Linear Algebra Machine Learning Basics
  • Data Science Data Science with 'Python' Deep learning Machine learning Neural Networks TensorFlow
The score is based on the quality of the course content & user experience as rated by the learners
Learning Experience
9.6
Content Rating
9.7
PROS:
  • Effective conceptualization on Neural Network and Deep Learning.
  • The Hyper parameter explanations are excellent.
  • Great coding exercises, improve understanding of the importance of vectorization
  • Deeper insight into how to enhance your algorithm and neural network and improve its accuracy.
  • Content delivery from a very experienced deep learning practitioner
  • Overview of existing architectures and certain applications of CNN's
CONS:
  • Need's organized structure of assignment & exercises
  • Automatic graders for programming assignments can be tricky
  • Assignment in the 5th course more challenging than other segments of the specialization

In the specialization, you will learn to build, train, and apply fully connected efficient vectorized Neural Networks. You will get the hang of identifying key parameters of these NNs and improving your DL models by tuning hyper-parameters (parameters whose value controls the learning process). Also, you will brush up on concepts you have already come across while learning ML, like regularization and optimization.

You will then move on to learn Convolutional NN, one of the most popular NNs. You will apply it to detect and recognize images. The course will also teach you Residual NNs (or ResNets), a less complicated and more efficient version of NNs.

Lastly, you will go over Recurrent Neural Networks (RNNs), which suffer from short-term memory. Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM), which are used to deal with this problem by retaining crucial information too are covered in the course.

This course in the Data Science Roadmap ends with interesting applications of DL we literally use every day – prediction text and Gmail’s automatic replies, or in more technical terms, ‘Character-Level Language Modeling’.

PROGRESS

90% progress

STEP 9: Big Data

Big data is exactly what the name suggests to be vast and complex datasets. The textbook definition of big data would be data that has the 3 Vs. – variety, volume, and velocity.

Big companies like Netflix, etc., deal with Big Data regularly to track customer demand and improve the user experience. You may wonder, how do their machines handle this data? The following specialization by UC San Diego teaches you exactly this and much more.

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9.2/10

Big Data Specialization

  • Coursera
  • University of California, San Diego
  • Microdegree
  • Self-paced
  • Beginner
  • 3+ Months
  • Free Trial (Paid Course & Certificate)
  • English
  • Apache Hadoop Apache Spark
  • None Pre-requisite
  • Apache Hadoop Training Apache Spark Training Big data Data Modeling Machine learning
Learning Experience
9.0
Content Rating
9.4
PROS:
  • Well structured course with a solid foundation and real-world problems
  • Provides a good overview and positioning of relevant big data technologies.
  • Step by step approach from basics of big data to Hadoop framework with hands-on mapping
  • Nice course to describe the traditional data modeling (RDBMS)
CONS:
  • Some exercises are a bit difficult to understand
  • The section on Spark needed more time and additional descriptions

You will learn about big data architecture in detail and will program models for scalable big data analysis. The course will employ the Hadoop framework, which makes dealing with big data less time-consuming and more efficient by applying distributive storage and parallel processing techniques.

You will learn about all three components of Hadoop:

  • Hadoop HDFS, the storage system
  • MapReduce, the processing unit
  • Hadoop YARN, the resource management unit and coding in Hadoop

You will also learn widely used Database Management Systems (DBMS), like AsterixDB, HP Vertica, Impala, Redis, Neo4j, and SparkSQL.

Owing to this course in the Data Science Roadmap, you will be able to integrate and process Big Data into any industry-standard software. In the end, the course will introduce you to tools that can help you actually do cool stuff by using this Big Data for Machine Learning.

PROGRESS

100% progress

Your Toolbox

Now, as a data science expert, you will have under your belt the following industry-standard data science tools:

  • RStudio: an IDE for R language
  • Jupyter Notebook: A notebook to keep your live code, equations, and text in.
  • Google Colab: A cloud-based version of Jupyter Notebook, which comes with a cloud-based GPU.
  • Excel: A tool that needs no introduction
  • Tableau: An interactive data visualization software
  • Power BI: An exhaustive analytics service by Microsoft
  • Hadoop: An all-in-one framework for dealing with Big Data
  • AsterixDB: A robust, open-source DBMS
  • HP Vertica: An analytic DBMS
  • Impala: An SQL query engine that provides low latency on Hadoop
  • Neo4j: A graph DBMS
  • Redis: An in-memory data structure store
  • SparkSQL: A module for structured data processing

Get Started With The Data Science Roadmap

Even though we have compiled an exhaustive guide for you to follow to become a data science expert, nothing is complete without getting your hands dirty. Even though all of the courses that we have recommended in the Data Science Roadmap will make you learn by doing, nothing beats trying something out on your own.

After you are done with a course, make sure that you apply whatever you have learned by doing a solo project based on the skill. In no time, you will have a shining portfolio to your name.

So, what are you waiting for? There is no better time than the present to get started!

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