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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

8 Thoughts on How to Transition into Data Science from Different Backgrounds

Pranav Dar
Pranav Dar Last Updated : 18 Nov, 2020
14 min read

Overview

  • Looking to transition into data science? Here are 8 paths for a non-data science person to land a role in this space
  • The 8 backgrounds we cover in this career transition article include – software engineering, finance, UX, application development, and a non-technical fresher
  • I’ve provided links to plenty of resources and learning paths to help you start your data science journey

 

Introduction

Are you looking for a role in the data science space? You’ve come to the right place!

It feels like half the world wants to move into data science these days, with spectacular perks and a plethora of openings on offer in the industry. Organizations are investing heavily in data science talent to stay or move ahead of their competitors. As a data science aspirant, you couldn’t have picked a better time to change your career!

But this comes with its own set of challenges. I’m often asked by folks in my network about how they should transition into data science. People from all sorts of backgrounds – IT, Sales, Finance, HR, Healthcare, etc. – they all want a piece of the data science pie.

Let me first put your doubts to rest – it is entirely possible to transition into data science from your current line of work (or study). And that’s what we’ll talk about in this article!

transition to data science

I’ve collated my pick of the top answers from around the internet in this article. We’ll be talking about how to make a transition into data science from these backgrounds:

  • Software Engineering/Development
  • Finance
  • UX (User Experience)
  • Application Development
  • Marketing and Sales
  • HR
  • Psychologist
  • A Fresher with no Relevant/Technical Background

If you are looking to transition into the field of data science, there is nothing more important than having the right plan and guidance. This is usually lacking from the general ways of learning (certification courses, blogs, tutorials). The AI and ML Blackbelt+ come with 14+ courses, 25+ projects, and the best part – 1:1 mentorship sessions so that you are never off track. 

Starting a data science career without proper guidance and planning can be confusing. We have compiled a clear-cut free roadmap guide to building a career in Data Science that is curated by expert curators at Analytics Vidhya – 

Let’s dive in!

 

1. Transition from a Software Engineer Role to a Data Scientist One – Yassine Alouini

This pick is for the software engineers out there looking for a transition into data science. Yassine has listed down the things you should do to get into data science. He also explains what kind of roles would be suitable for a software engineer getting into this field.

Answer by – Yassine Alouini

To start with, if you really enjoy software engineering, then you should consider becoming a data engineer or a machine learning engineer.

These aren’t technically speaking “canonical” data scientist roles, but close enough and considered among the larger data science fields.

If you would still like to become a data scientist, then you should work on these skills:

  • Basic Probability and Statistics: Nothing too fancy, just the rudimentary stuff
  • SQL: You are probably familiar with this (weird) language. You have probably used an ORM to interact with different databases. Learn a little bit more about it: window functions, CTEs, triggers, good SQL style guide, and so on
  • Modeling: Again, nothing too fancy. Learn some good models to use and when to use them. Read the documentation and tutorials online when needed. This skill also requires domain knowledge about the things you are working at (ranges from health insurance to warehouse logistics)
  • Data Visualization: Data analysis is not very valuable until you turn it into a graph: could be a map, a time series, a 3D pie chart (just kidding, don’t do that please), or anything else
  • Reporting: Once you have some solid insights, you should make it available and organized into a compelling report. It could be a document or a dashboard (always prefer these)
  • Communication: Finally, you have produced a report and/or a dashboard. You should be at ease when discussing it with your colleagues and superiors. This is a very hard skill to master but totally worth it in the long (and the short!) run

You’ll love the two stories I’ve mentioned below as well about how two software engineers successfully transitioned into a data scientist role. The articles detail the learning path these people took to achieve their dream:

 

2. Career Transition from Finance to Data Science – Richard Saldanha

Finance seems like a natural fit for data science, doesn’t it? It’s a numbers field and that blends in nicely with the data science space. It’s no coincidence that the BFSI sector is leading the way in data science adoption!

So if you’re coming from an accounts/finance background, you’re already halfway to achieving your dream of getting a data science role.

finance_data_science

Answer by – Richard Saldanha

If you want to work as a data scientist in finance, you will probably need most (if not all) of the following attributes:

  • A degree in mathematics/statistics, computer science, physics, engineering, or subject with significant mathematical content
  • An ability to program in multiple languages (both compiled and interpreted) such a C/C++, S (e.g. as implemented in R), Matlab, Python, and/or Java
  • Good database skills (i.e. at least SQL programming) in any classical RDBMS (for example, MySQL, PostgreSQL, Oracle, SQL Server)
  • An adeptness with handling time-series data from Bloomberg, Reuters, or any of the myriad financial data streams available

However, there are also two very important characteristics of people doing data science jobs in finance which are less frequently discussed:

  • You’ll need to be able to communicate mathematical ideas well both verbally and visually to non-specialists
  • You’ll need to know how to harness their mathematical training to solve genuine commercial problems

Alongside all this, you’ll need a good understanding of optimization (underpinned by solid linear algebra and calculus learned in school), statistical inference, simulation, multivariate analysis, and proper data visualization.

If you possess such training, then understanding techniques such as support vector machines, neural networks, random forests, and gradient boosting are merely a hop, skip and jump away.

I also recommend checking out this article which talks about the applications of data science in the finance industry.

 

3. Should a UX Designer/Researcher Become a Data Scientist? – Chris R. Becker

This is an intriguing career transition! I’ll be honest – I hadn’t considered a UX person wanting to transition into data science.

This answer by Chris R. Becker focuses on learning data science tools keeping UX experience in mind. He states some of the tools which UX designers are already using and how those tools could be used for data science purposes. He emphasizes more on working with a data science team to dive deeper into critical data science topics.

UX_data_science

Answer by – Chris R. Becker

UX researchers are more often than not already using data (qualitative and quantitative) tools. Most are already using low hang data science. This includes Google Analytics, surveys, user polls, Excel, JSON, and user testing data (among other things).

These are data tools and methods essential to doing UX and finding patterns in the data. However, they are limited in scope and reach.

If you want to dive deeper into data science tools and languages, this is where it gets more complicated. You could learn data tools like advanced Excel, Tableau, or sci-sense or learn code like JavaScript with data libraries like D3.js or R. These tools and code syntaxes are much harder to learn.

As a UX researcher, I would much rather work with a data scientist than have to learn a whole other profession to do my job effectively. I think it is crucial to know what is possible with data and then seek out that expertise as needed. Obviously, fluency in data science tools will go along way in collaborating with a data scientist in this case. Seek out team members who can help you find new and relevant patterns in your research data.

 

4. How I Successfully Switched from Application Development to Data Science – Ankita Ghoshal

I took this answer from an Analytics Vidhya article itself given how relevant it is. A lot of application developers want to transition into data science but are unsure if they are qualified enough for it.

This article by Ankita Ghoshal will put any doubts to rest! She explains, in thorough detail, how you can make this career transition successfully if you get started now!

app_developer_data_science

Answer by – Ankita Ghoshal

The best way of penetrating into a new field is by first understanding the current technologies. The buzzwords back in 2016 were, as you might have guessed, ‘Data Science’ and ‘Machine Learning’.

I had vaguely heard about these terms through online articles. I started exploring career options in this field and found that Statistics was the base of Data Science. This meshed perfectly with my interests – Statistics has always fascinated me. There is nothing better than working in a field that you love!

A quick Google search on ‘Analytics Machine Learning Tutorials’ led me to India’s largest data science community, ‘Analytics Vidhya’. I went through their articles on educational institutions providing courses for careers in Data Science.

I spent most of my professional career in programming before switching to Data Science. As a kid, I studied statistics but those concepts were long forgotten (as I’m sure you’ll relate to!). Making this transition was indeed tough, but not impossible.

The key is to never stop learning. During this switch, I realized that you don’t need to unlearn your existing skills to pick up a new one. I used my programming skills as a bridge between IT and Data Science to structure my machine learning code more logically.

This transition also helped me understand that the presentation of project results varies significantly from industry to industry.

For example, in the IT industry, the output of a web development project is a web page that is completely understandable by the stakeholders. In the world of data science, the output is (usually) numbers. It is the role of a data science professional to reveal these numbers to the customers/stakeholders using an indicative story.

For those who are ready to start your transition into Data Science, I recommend reading the below suggestions carefully:

  1. Ask yourself this – are you are really interested in data science and are you a good fit? Don’t just fall for the glamour and hype. There are plenty of resources available online, such as the various articles and blogs on Analytics Vidhya, to get a feel of what this field is all about
  2. Statisticians and programming professionals will undoubtedly have a bit of an advantage. But here’s the good news – the transition is possible even for non-technical people. The primary thing that matters is your thought process and skill of questioning and analyzing the information at hand
  3. If you are an experienced professional in any other sector, get ready to be treated as a relative fresher when you make the switch to data science. It might be quite difficult for many people to accept a transition where you have to forego some years of seniority. I understand that. But if you give your best, then this industry will bestow even more knowledge, greater successes, and awesome salary hikes on you

Note: You’ll also love the below story about how an IT person, after working in the field for a decade, transitioned into data science:

 

5. Can a Marketing and Sales professional switch to Data Science? – Ayush Biyani

Marketing and Sales experience is quite different than any other career transition mentioned. The sales and marketing team work very closely with analysts and rely heavily on data. There is no doubt that many professionals will be lured by data science. This can be a natural transition so read ahead to know more.

This answer by Ayush Biyani is the bitter truth for anyone wanting to start their career in data science. Ayush mentions the skills required to become a data scientist, how your day looks like as a data scientist, and asks you to decide for yourself.

Answer by – Ayush Biyani

I see two other answers where they advise you to switch to analytics if you like it. I would like to point out a few realities to you so that you know what they all assumed you have. I assume you have some experience under your belt in finance.

  1. Analytics job is not your other routine job where people have a feeling “I can manage it somehow “. You are either in it fully or not there. There is no middle way out. so don’t flatter yourself by saying “Mai manage kar lunga”
  2. Analytics requires respect and knowledge both for the following entities: Maths in general, statistics, number crunching, programming ( to some extent )
  3. Analytics is supposed to make somebody take some decision that brings some profits or cuts some loss ( make a better decision in general ) so it requires a lot of brutal business focus. Trust me this is not as easy as it sounds. I have been seeing it for 7 yrs now.
  4. Analytics tasks require structured thinking. I have seen that this is where most of the people who are new to its struggle and then give up. Ask yourself – can I think structurally boring manner for the rest of life. Is it me? Only if the answer is yes should you consider shifting.
  5. Since you are new you have to swallow your “Area head” ego in most of the scenarios and be willing to learn and at times unlearn the way things work. Ready for it? ready to work with a fresher better suited at these tasks without an MBA and 5 years younger than you.
  6. Are you ready to play with terms such as “regression”, “decision tree”, “logistic regression”, “cross tabs”, “graphs and charts”, “feature engineering”, “model validation” for your whole life?
  7. Behind all the glamour that people see in analytics – there is boring maths and stats that are sitting and doing their work silently. Every presentation and graph have a number at the background taken by more than a simple method. appreciate that?
  8. This one is most important- Do you really consider yourself a keen and a fast learner? Yes? Sure?

In my view, you have to answer at least 5 out of these points as yes. And (h) and (f) has to be yes for sure.

Look, my aim is not to discourage you but give you the actual and real-not so rosy picture. I daily see analysts making faces when some sharper analytics guys come and tell them that this analysis is not right for Xyz reasons.

So…Be realistic and take a practical call. Not a glamorous call.

Motivated? I have added a few resources to help you navigate through marketing and data science –

  1. Power of Marketing and Business Analytics – An Approach to Grow your Business Online from Scratch
  2. A Comprehensive Guide to Digital Marketing and Analytics Every Data Science Professional Must Read

 

6. Going from HR to a career in Data Science – Ann Rajaram

Honestly, when people hear transition stories from HR to Data Science, they find themselves dumbfounded and the usual reaction is this – “Are you serious? Is there any connection to these fields?”

Ann Rajaram answers this question for you. She demonstrates how you can transition your career into data science and make use of the precious “people skills”.

Answer by Ann Rajaram

Technically, anyone can become a data scientist, assuming you (a) master the programming skills and (b) show potential employers how you can add value.

But if you already have strong experience in HR, then it makes logical sense to leverage that domain expertise, too. For example, a lot of the larger companies use data scientists in the HR domain for “workforce analytics” to understand employee churn, project ROIs, leadership development and to improve diversity metrics.

For example, a lot of the larger companies use data scientists in the HR domain for “workforce analytics” to understand employee churn, project ROIs, leadership development and to improve diversity metrics.

Not saying that you cannot pivot 100% to a completely different domain, just that it would be hard to start from scratch without either domain knowledge or serious technical chops. Occasionally, salaries at entry-level data scientist roles might be a step down for someone who has 7–10 years of corporate or managerial experience.

Models used for workforce analytics would also translate well to universities and educational/behavioral health institutions since these are all domains requiring “people skills” and a strong understanding of behavioral psychology. For example, large universities use analytics from learning management systems to (a) intervene for students at-risk of failing, (b) create personalized student paths, or (c) even update curriculum to spend more time on syllabi where a large majority of students trip or struggle.

The AI and ML Blackbelt+ come with the guidance of an expert mentor who will customize the learning path specifically for you. Coming from an HR background and don’t know where to start? AI and ML Blackbelt + program can help you there.

 

7. Break from Psychology to Data Science – Philippe Hocquet

Honestly, this is by far the most interesting section of the article. Let us not forget that Data Science is a subsection of Artificial Intelligence. A psychology professional may have many insights that are invisible to a normal person.

Philippe Hocquet enlightens us with his thoughts on Psychology and machine learning and how can a psychologist contribute to a data science project. Read ahead for the intriguing answer.

Answer by – Philippe Hocquet

The key is to move as fast as possible to an actual project in your area. Be the psychologist in a team, write about it, meet with people in the field, and find a next job that is 80% Psychology, 20% Machine Learning, then go from there.

Data Science is a sports team by excellence: on top of the Maths Ph.D., a project also needs systems engineers, designers, psychologists, healthcare professionals. That’s your foot in the door.

One area I would start with is the Human-Computer Interface (HCI). For instance:

  • How people can communicate emotions to the machine? Which ones and why?
  • How the systems can be made more efficient by taking into account frustration, stress, pushback from the user?
  • How to generate expressions in synthetic speech?
  • And even: how the algorithm can become emotionally intelligent, i.e. take into account human psychology in its evaluation of scenarios/impact

I can think a dozen of companies or labs that employ plenty of psychologists. This is US-based but you can probably find the equivalent in Germany:

It goes from “empathic tech” (for example Rosalind Picard group at the MIT media lab: Group Overview ‹ Affective Computing – MIT Media Lab) to healthcare applications (for example Ellie developed at the USC, for clinical interviews, the Simsensei project. Paper here: Morency).

And of course, an entire industry now exists to measure, track, and extrapolate every nuance of human behavior online (think the “manipulative” or “addictive” features of apps).

 

8. There is No One Background Required to Become a Data Scientist – Arun Korupolu

One of the most common questions we see is – can I become a data scientist without a technical/engineering background? The short answer – yes!

The below answer by Arun summarizes our thoughts perfectly. You don’t necessarily need a Ph.D. or even a programming background to start (though that might be helpful if you have that experience!).

Data_scientist

Answer by – Arun Korupolu

There is no background required for you to become a data scientist in the long-run, it’s all about your interest and you asking whether you are interested to work with data and envisage yourself in a role where data and decision making are aligned.

I can suggest a high-level learning pathway, but individual learning needs with regards to time and effort might require appropriate tweaking in these steps.

Beginners ideally would need to start learning programming, in case they have no prior experience. You can follow this learning in three steps:

  • Learn Programming (R or Python) and become proficient in that language
  • Gain the knowledge in these subjects – Intermediate Statistics & Probability, College Algebra, Linear Algebra, Machine Learning algorithms and methods
  • Work with independent projects. Try to implement your learning in a step by step fashion while solving the objectives of these projects

My suggestion is a high-level overview of what you can do to start, but you will find the best path once you begin learning by doing.

Note: You can also check out Analytics Vidhya’s comprehensive and free learning path to become a data scientist in 2020.

 

End Notes

Are you looking to transition into data science from one of these backgrounds (or a completely different one)? Our community would love to hear your story! Stories and experiences like these help future career transitioners as well.

As I mentioned at the start of the article, you should definitely enroll in either of the two learning paths depending on your career aspirations:

If you are pumped and motivated to start your career in the field of data science, you can go through these comprehensive guides to become an industry-ready data science professional

  1. Download this free Comprehensive Data Science Roadmap to start your career
  2. 14 Must-Have Skills to Become a Data Scientist (with Resources!)
  3. A Comprehensive Step-by-Step Guide to Become an Industry-Ready Data Science Professional
  4. 12 Essential Tips for People starting a Career in Data Science
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Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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Responses From Readers

Clear

Rajesh
Rajesh

Nice article. Can you please provide path for people wants to go from academics to data science industry?

123

Manoj Sharma
Manoj Sharma

Hello Pranav, This is a very informative article which encouraged me to move forward to become a data scientist. Although it covered 5 different five different however my background if completely different from these five. I have 12 years of experience of IT Service Management (ITSM/ITIL). Could you please guide me that will it be possible for me to transit myself into data scientist role and if yes what all I need to have to become a data scientist and how much time this journey would require. Appreciate your response.

123

Natasha
Natasha

Data Science is vast field, Reading books or PDF will not help you unless you do hands on practice. you need to follow proper learning path to understand depth of each and every concept in Machine Learning & Deep Learning. If you are fresher and wanted to make your career into Data science its very good choice but at the same time you have to be focused on what you are learning. If you are already into industry and wanted to switch your career into Data science you must need to work on data from multiple Domain and build projects on it which will surely help you to crack interviews. I have done course in Data science from Business Toys. I highly recommend the course from Business Toys if you want to take real experience in the world of Data.

123

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