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Reading list

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

Planning a late career shift to Analytics / Big data? Better be prepared!

Kunal Jain
Kunal Jain Last Updated : 25 Jun, 2019
5 min read

Introduction

I feel lucky to be part of the data revolution happening around us. Because of the attention and the focus on Analytics / Big Data, there are quite a few people considering a late career switch.

Whether you’re a fresher, a mid-career transitioner, or considering a late career switch, you need to be extremely well prepared to crack data science interviews. That’s what we aim to help you do in the ‘Ace Data Science Interviews‘ course! Enroll today and enable yourself to land your first data science job!
planning a late career shift ?

I get quite a few queries asking my advice on this matter regularly. Here are a few samples (after removing names):

I am an equity analyst (finance background) and have 10+ years experience. I don’t have any background in IT. I have a background in chemical technology (BE) and MBA finance.

After all these years and entirely different background; how easy/difficult is to pursue fresh career in these areas – Analytics and Big data. Will there be opportunities for such diverse background on immediate and Long term basis.

Here is another one:

I have 6+ years of experience in IT consulting, none of them is related to Analytics. But I want to move into analytics. Is there a way to do that? Please advise. 

Yet another one:

I came across this site as a part of my quest to change my career track. I have 10 years of experience , out of which last 5 years is in institutional relationship management cum acquisition in banking sector. Prior to that I have 1.5 years sales experience in FMCG sales. I have done MBA(mktg) and B.E. Please guide me,
1. Would my existing profile be adaptable to career in business analytics?
2. How easy (or difficult) it would be to get job in business analytics?
3. Which course and from which institute would relatively assure me of a job immediately in analytics?
Thanks

Hence, I thought of expressing my thoughts for the benefit of a bigger audience. Also, through discussion with other people in this domain, I am hoping to pull in broader views on the subject.

Take the Test: Should I become a Data Scientist?

Please note that rest of this article assumes that you are looking for a hands on core analytics role (where you analyze the data yourself) and not a supporting role (e.g. Pre-Sales for Analytics products, Business development for a consultancy)

[stextbox id = “section”]A few facts you should consider:[/stextbox]

  1. The rosy picture (recruiters running behind you) you thought, would be at least 3 – 5 years away. The first few years would be very arduous. More so, if you don’t need to be an Individual Contributor in your current role.
  2. Analytics / Big data is a knowledge intensive domain. You can do well only if you gain knowledge and work hard regularly.
  3. A lot of good companies believe in hiring smart people straight out of college and training them internally. These Organization would not think about hiring you.
  4. In an ideal world, there should be no bias against people with experience. They should be judged only on the knowledge and skills they possess and their willingness to learn / work. Sadly, this is not true. A career shift (especially late in career) is often looked suspiciously, until you have received outstanding recognition in your previous roles.

[stextbox id = “section”]Would your work experience be considered?[/stextbox]

The answer depends on the domain of your experience:

  1. Non technical experience will not count in your analytics jobs – the only benefit you might get is that the interviewer can expect you to be more mature with your thought process / decision.
  2. Technical experience (programming / SQL / server) gets counted, but will only be considered as equivalent of 1 – 2 years experience.
  3. Consulting / research would count more. May not be counted 100%, but healthy fraction would be considered.

[stextbox id = “section”]How soon can you get a job?[/stextbox]

Well, bake in any where between 3 months to a couple of years depending on your learning agility, problem solving skills, communication and presentation skills. If you are outstanding on these attributes, you should get a job as soon as you get some technical skills under your belt. An average person typically takes 6 – 12 months after gaining necessary technical skills. If you take any thing more than 12 months, you should try and re-assess what is going wrong.

P.S. This would vary from geography to geography. The above answer is applicable in markets where there is big un-fulfilled need of analysts.

[stextbox id = “section”]OK, you have thought enough. You are absolutely sure that your life would not be complete without a shot at analytics. What next?[/stextbox]

In case you are absolutely sure, here are the next steps:

Once you have followed these blogs / communities for a while (say at least a month after your course at Coursera), you can look out for certification courses to begin your journey.

[stextbox id = “section”]My final advice:[/stextbox]

If you are not in deep love with data and can not spend hours slicing and dicing data in front of a computer, analytics may not be your cup of tea.

Take this up only if you tick all the boxes below:

  • You are absolutely crazy about this industry. You can’t help but analyze any numbers you come across – I play with numbers on the number plate of any vehicle which passes me.
  • You have undergone a few courses and have excelled at them. You have submitted all the assignments and have scored extremely well.
  • You have the perseverance and motivation to undergo 2 – 3 years of arduous work learning about a new knowledge intensive domain.
  • You are willing to spend a lot of time as Individual Contributor

Lastly, if you have family support in this move / decision, it will make the move less painful (or more enjoyable depending on the way you look at).

P.S. The aim of this article is not to dissuade people wanting to join Analytics Industry. I know a lot of people, who made this switch in middle of their career and they have not been more happier in their career ever before! Their passion for analytics and numbers comes across even if you spend 5 minutes talking to them.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

samir
samir

Nice job !!

123

Tuhin Chattopadhyay
Tuhin Chattopadhyay

Excellent article. A course on analytics from IIM/ ISB/ abroad could also enhance the probability of success.

123

Datta
Datta

Hey Tuhin, You pointed out an interesting thing, please have a look into this article if you missed it. :) http://www.analyticsvidhya.com/blog/2013/07/advanced-analytics-certifications/

123
Dheeraj Chowdhary
Dheeraj Chowdhary

Hi Kunal, Very informative article but you missed mentioning some good courses on Coursera / eDX. Request to kindly mention the same in the article. Thanks,

123

Kunal Jain
Kunal Jain

Dheeraj, Have added them. Thanks for pointing this out. Regards, Kunal

123

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