Data Science learning path for beginners

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Data Science learning path for beginners

Data Science is very vast field which requires knowledge of many fields. In this article we are going to discuss best Data Science learning path for beginners.

Data Science is very vast field which requires knowledge of many fields. In this article we are going to discuss best Data Science learning path for beginners.

Data Science Learning Path: Data Science learning path for beginners

Welcome to our complete guide for the beginners section, here you will find all the information you are looking for "data science learning path for beginners". In this guide we are going to provide you with the best learning path that a beginner should follow to learn and master Data Science by own or taking online training. Whether you are learning Data Science yourself or taking courses for learning Data Science this guide is for you. You will find all the information necessary for getting started and mastering the Data Science technologies.
This is the ultimate guide for the beginners to become Data Scientists from the beginning as it provides a clear path of learning Data Science from scratch. We are super excited to present you the step by step guide to learn and master DL/ML techniques.

What are the prerequisites for learning Data Science?

First of all we will see the prerequisites for learning the fast growing Machine learning and Data Science. You are not expected to have a data science background for getting started with data science, but prior experience in programming is must. If you have worked on Python, Java, C, C++ or any other programming language and have experience in commercial application development then you can learn Data Science.

Data Science Learning Path for beginners

Here are the prerequisites for learning Data Science:

  • Bachelor’s degree in science, relevant field like computer science, statistics, applied mathematics
  • Prior experience in programming languages such as Python, Java, C, C++ or any other programming language
  • Prior experience in development of commercial application using above programming languages
  • You should have a solid understanding of Mathematics used in Data Science - If you have learned Mathematics during your degree course then it's time to refresh it before diving into Data Science.

Data Science learning path for beginners

Data Science is one of the fastest growing fields in information technology and there is a shortage of a large number of expert Data Scientists around the world. Data Scientists are getting paid the best salary in the IT industry. According to various industry reports there is a huge shortage of skilled data scientists. So, there are still a lot of job opportunities for programmers having experience in Data Scientists. You can learn Data Science and try to fill the massive shortage of Data Scientists in the industry. There are many big brands like IBM, Google, Microsoft, NVIDIA and others offering courses in Data Science to fill the Data Scientist shortage in the industry. Out of these offered by big brands some courses are free as well.

Data Science jobs are highly paid jobs around the world and yearly salary for a Data Scientist might go beyond $120k. Data Scientists play a very important role in analyzing the data for business and present meaningful predictions for business. If you are ready to take this challenge then learn and master Data Science. This post reveals the steps for learning Data Science from scratch and master it by learning advanced topics.

If you are planning to make a career in the field of data science then this is the right time and you will be able to get highly paid jobs after learning data science. This article presents you with a complete guide for learning learning and mastering Data Science.  This  post provides you a Data Science learning path for beginners that lists down all the topics you should learn to master Data Science.

What are the basic requirements for a Data Science job?

If you check the job site to see the requirements posted by top companies you will get to know the basic requirements of applying for data Scientists.

Almost all the jobs posted on job sites are looking for the bachelor's in the relevant field like computer science, statistics, applied mathematics, and so on. If you have a bachelor's degree in a relevant field and have experience in data science you will be able to apply for the job. If you track the job market you might see highly paid jobs as well.

So, the bachelor's degree in the relevant field is a must for most of the Data Science jobs in the market.

What to learn in Data Science?

First of all we will see what to learn in Data Science? Data Science uses various machine learning and deep learning techniques to achieve the organizational goal. So, as a data scientist you have to learn machine learning and deep learning techniques. 

Learn And understand the job role of data scientist

First of all you have to  understand the job requirements, job role and responsibilities for you as a data scientist. Once you understand this you will be able to map your learning skills with the industry requirements. You will be able to focus more on the most demanding skills based on the current job trends in the market.

Technologies you should learn

On the programming technologies side Python, Keras and TensorFlow is being used for machine learning/deep learning. So, you will have to learn these technological skills. I would suggest you to go for Data Science and Big Data online training. But, you may also go for a full time University based degree course in data science if you have time to attend a full time degree course.

Apart from programming skills you will have to learn Maths skills also. You may go for any crash course or short term course in Mathematics for Data Science. You don't have to be an expert in Mathematics, instead you should learn statistics, probability, linear algebra etc…

Data Scientists spend over 70% their time cleaning and preparation of data for training their model. You should learn how to do data gathering, data cleaning and data preparation for data science. So, data preparation is also a mandatory skill for data Scientists. Python, pandas and Numpy are widely used in the data preparation phase. Professionals working in the data science project should learn these skills.

Mastering Deep Learning

After completing your studies in machine learning you can start with Deep Learning. These days Deep Learning is used to solve many of the AI use cases. Deep Learning is the application of Artificial Neural networks for development of ML/DL models to solve real life use cases. Deep Learning is one of the most asked skills in Data Science jobs.

What to learn in Deep Learning?

The Deep Learning techniques are based on human neural networks and they try to mimic human-like intelligence. Deep Learning Artificial neural networks (developed using various mathematical algorithms)  are used which play a very important role in the model.  A large number connected neuron layers are used to achieve the intelligence in the deep learning models.

There are many different types of neural networks used in the industry to solve various problems. You should select the models to learn based on your current job requirements and your personal interests.
Here are the topics you should learn in Deep Learning:

  1. Neural Networks
  2. Convolutional Networks
  3. AutoEncoders
  4. Deep Belief Networks
  5. Recurrent Neural Networks
  6. Long Short Term Memory
  7. Deep and restricted Boltzmann Machines
  8. Deep Reinforcement Learning

TensorFlow 2 is the most trending Deep Learning model which comes with Keras libraries for easy development of models. So, you should learn Python, TensorFlow 2.x and Keras to master Deep Learning.

Other deep learning libraries that you may learn are:

  • TensorFlow
  • Keras
  • Caffe
  • Microsoft Cognitive Toolkit (Previously CNTK)
  • PyTorch
  • Apache MXnet
  • DeepLearning4J
  • Theano
  • TFLearn
  • Torch
  • Caffe2
  • PaddlePaddle (PArallel Distributed Deep LEarning)
  • DLib
  • Chainer
  • Neon
  • Lasagne
  • Apache SINGA

What are the topics that I should learn to become an expert Data Scientist?

Now we will see the listing of topics that you should learn step by step to dive into Machine learning and Data Science. You will have to learn a lot of topics to become expert Data Scientists. Let’s see all the topics necessary in Data Science:

1. Fundamentals

Matrices & Linear Algebra
SQL, NoSQL and various data formats
JSON and XML data formats
Basics of ETL
Data visualization and reporting
Data Analytics
Working with SQL and Relational databases

2. Statistics

In Statistics you have to learn Conditional Probability / Baye’s Probability, Mean and Distribution, Mean Square Error, Least Squares,  Regression (for prediction), Nearest Neighbors (for Classification), Statistical Decision Theory Statistical Model for Join Distribution  and few others. If you want to  see all the topics you should learn in statistics then check our tutorial What to learn in statistics for data science?

3. Programming

There are many programming languages for the development of the Data Science model but as a beginner you should learn Python for Data Science. Here are the list of all the top programming languages used in Data Science:

  • Lists, Arrays, Vectors, metrices, Variables, Expressions and other programming concepts in programming language you selected for Data Science
  • Python Programming
  • Java Programming
  • R Programming
  • Working with Excel for Data Science

4. Machine Learning

In Machine Learning you should learn following:

  • What is Machine Learning and its Types?
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Data gathering, data cleaning and data preparation for training model
  • Understand training and Test data
  • Machine learning concepts like Classifier, Prediction, Lift, Overfitting, Bias & Variance, Trees & Classification, Classification Rate, Decision Trees and Boosting
  • Naive Bayes Classification
  • K-nearest Neighbor
  • Logistic Regression
  • Ranking
  • Linear Regression
  • Perceptron
  • Hierarchical Clustering
  • K-means Clustering
  • Neural Networks
  • Sentiment Analysis
  • Collaborative Filtering
  • Tagging

5. NLP and Text Mining

Natural Language processing (NLP) and text mining is very popular in machine learning. If you are planning to work on the text processing projects then you should learn NLP.

  • Introduction to NLP
  • What are the machine learning techniques in NLP?
  • Vocabulary Mapping
  • Classify Text
  • Using NLTK
  • Using Weka
  • Using Mahout
  • Understand the Feature Extraction
  • Examples of Market Based Analysis
  • Understanding the Association Rules in NLP
  • Support Vector Machines
  • Term Frequency and Weight
  • Term Document Matrix
  • Apache UIMA
  • Text Analysis
  • Named Entity Recognition
  • Corpus
  • Other use cases of NLP

6. Data Visualization

There are many tools for Data Visualization but won’t have to learn all these. You should learn at least Data Visualization libraries in Python which is necessary for day to day Machine Learning work. Here are the list of top data visualization tools/libraries that you might have to learn during your career as Data Scientist:

  • Matplotlib
  • Plotly
  • Seaborn
  • ggplot
  • Altair
  • Tableau
  • IBM ManyEyes
  • InfoVis
  • D3.js
  • Decision Tree
  • Timeline
  • Survey Plot
  • Spatial Charts
  • Line Charts
  • Scatter Plot
  • Tree & Tree Map
  • Histogram
  • Pile Chart

7. Big Data Technologies

As a Data Scientist you should learn few of the Big Data technologies as you have to interact with the data stored in the Big Data environment or you may have to deploy your machine learning model on the Big Data cluster. Here are the technologies of Big Data that you should learn:

  • Hadoop
  • hadoop Components
  • HDFS
  • Map Reduce Fundamentals
  • Setup Big Data platform(Cloudera/ HortonWorks)
  • Understanding Name and Data Nodes
  • Working with the Hadoop Cluster
  • Understanding Job & Task Tracker
  • M/R Programming
  • Learning Sqoop for data load from relational databases
  • Flume for Unstructured Data
  • Hive and Pig
  • Basics of SQL and NoSQL
  • Working with the Hive
  • Understanding and using various Hive Data formats
  • Learn Cassandra
  • Learn MongoDB and Redis

8. Data Ingestion

Data Ingestion is a major part of the Big Data System where a large amount of companies business data is stored for further analysis. There are many ways to analyze the data stored on the Big Data cluster and data science is of them which provides reports by running various machine learning models on the data. You don’t have to be an expert in Data Ingestion but you should be able to understand all the concepts of Data Ingestion and be able to work if need arises. Here are the topics you should learn in Data Ingestion:

  • What is Data Ingestion?
  • What are the ways of Data Ingestion?
  • What is the importance of ETL?

9. ETL (Extract, Transform, and Load) Process

The ETL (Extract, Transform, and Load) Process plays an important role in today’s Big Data and Data Science environment. The ETL process is responsible for fast ingestion of data into Big Data clusters. As a Data Scientist you should learn following ETL concepts:

  • What is Data Warehouse?
  • What is ETL?
  • Why do you need ETL?
  • Steps in ETL (Extraction, Transformation and Loading)
  • What are the ETL tools?
  • Best practices in ETL process?

As a data scientist you will have to load the data from various data sources, prepare the data for model training and finally train the model with the data. For the training and validation process you have to prepare the data correctly so that your model is trained with the good data.

10. Data Munging/Data Wrangling

Data Scientists spend around 70-80% time in the preparation of data for model training and this process is very important which takes considerable time. You should learn following topics:

  • What is Data Wrangling?
  • What are the steps of Data Wrangling?
  • What are the tools of Data Wrangling Steps?

11. Top tools of Data Science and Big Data

As a Data Scientist you will have to learn the top tools used in Data Science and Big Data. These are the technologies which is being used in today’s Big Data and Data Science environment:

  • MS Excel
  • Java, Python, R, R-Studio and Jupyter Notebook
  • Weka, Knime and RapidMiner
  • Hadoop and Big Data Platform
  • Spark, Hive, Storm, Flume, Scibe, Chukwa
  • Nutch, Elasticsearch, Apache Solr, Scraperwiki
  • Talend
  • D3.js
  • ggplot2
  • Cassandra MongoDB

In short to become productive Data Scientist you have to learn and master the following skills:

  1. Programming skills
  2. Statistics and Mathematics
  3. Machine Learning Algorithms
  4. Data Intuition
  5. Data Visualization
  6. Communication

After learning all the above or most of the above topics you will find yourself in a good position to work on the Data Science projects and produce results for your employer.

Now we will see how to learn all these topics.

How to learn Data Science?

There are following major options for learning Data Science:

  1. Self-Learning
  2. Fast paced online training classes
  3. Six months to a year online training classes
  4. Regular college degree in Data Science

How to self-learn Data Science?

If you have real zeal and want to learn Data Science then no one can stop you. You will be able to learn it yourself through online tutorials and various YouTube videos. Although it will take time and huge effort to learn the topics yourself, it's not impossible; you will be able to learn all these topics of Data Science yourself. Just go through the topics explained in this article one by one and learn it through books or online tutorials. After spending around 3 to 4 hours per for 6 months you will be able to learn most of the technologies. After learning all these techniques you can start applying for the Data Scientist position.

Fast paced online training classes/Six months to a year online training classes

If you want to learn Data Science through online fast track courses then check out our training section at:

You may also join the courses offered by IBM, Google and Nvidia.

Regular college degree in Data Science

If you prefer to go for full time degree course then checkout following courses:

  • Manipal Institute of Technology, India – B.Tech In Data Science and Engineering (On-Campus)
  • University of Manitoba, Canada – BS in Data Science  (On-Campus)
  • Purdue University, USA- MS Business Analytics (Online)
  • University of Texas, USA – MS In Data Science (Online)
  • IIT Madras, India- BSc In Programming and Data Science (Online)

What are the Job roles in Data Science?

There many job roles in the Data Science filed and you should aware of these job roles. Here are the top job roles in Data Science:

  • Data Scientist
  • Data Architect
  • Data Engineer
  • Statistician
  • Data Science Manager
  • Machine Learning Engineer
  • Decision Scientists

You should check roles and responsibilities of each job roles in Data Science which will help you in preparing for the job interviews.

In this post we have discussed the Data Science Learning path for beginners. You can checkout following tutorials on Data Science:

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