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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

How to start a career in Business Analytics?

Kunal Jain
Kunal Jain Last Updated : 27 Aug, 2021
4 min read

Every time I attend any analytics forum or interact with students, two questions stand out on account of number of times they are asked:

I am an undergraduate (or any other) student & want to pursue career in Analytics, what should I do?

or

I want to build a career / shift my career in Business Analytics, how should I go about doing so?

Further, I receive multiple queries through mail / social media / comments asking the same. In order to make sure these queries are addressed in best possible manner, I thought it’s best to write an article on this and provide a starting platform for everyone.

How to start a career in Business Analytics

While the subject itself is wide, I’ll provide an overview of various things which can be done and focus on specific sub-topics through additional posts later on.

Before I delve into the ways of building an Analytics career, let me quickly cover why to build a career in Analytics first.

 

Why build a career in Business Analytics?

Analytics, as an industry is set for exponential growth. With more and more data being available in digital form, need for smarter, faster, data based decisions is only going to increase. Consider following facts to substantiate what I am saying:

  • According to Harvard Business Review (October 2012 edition), job of a data scientist is the sexiest job of 21st century.
  • According to the McKinsey Global Institute (In a May 2011 report): “By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”

Imagine what would be the number across the globe…

 

I understand Analytics will become big, but I don’t know how do I start a career in Business Analytics?

Its difficult for a newbie to find out the best way to start a career in Analytics due to following reasons:

  • The industry in nascent stage, hence, it is difficult to find proper guidance.
  • “Analytics” or “Business Analytics” are losely used terms, which makes it difficult for people to understand and appreciate the role
  • There are limited structured & well laid out path which you can follow to enter the field.

While the first factor will improve with time and I had written an article mentioning second point here, this post aims to address the third point.

 

Two approaches to build a career

Like any other employment skill, there are 2 approaches to enter / start an analytics career:

  • Approach 1 – Get hired by a company which trains you (on job / internal trainings) on the necessary skills. These would be companies which have Analytics in their DNA and use it for their day to day decisions. While this approach is better from long term perspective, it takes time and investment (especially if there is no structured training in the companies). Some of the companies known for using cutting edge Analytics (in India) are:
    • Technology leaders: Google, Facebook, Linkedin
    • Banking, Financial Services and Insurance (BFSI): Capital One, American Express, ICICI, HDFC
    • Telecom companies: Idea, Vodafone, Airtel
    • Analytics Consultancies: Fractal, Mu-Sigma, Absolutdata, ZS Associates

We have also prepared an comprehensive course on how to ace data science interviews. If you are expecting to face an interview with any of these companies, that’s your jackpot!

One of the alternate route to get into these companies can be internships. So if you have a 2 – 6 month break, give a try to becoming an intern in these companies.

  • Approach 2 – Get Business Analytics related certification: While these certifications provides you with the technical skills required, these would not be able to compensate for experience on the job. Following are some options of training available (in India):
    • For people with work experience, various leading academic institutes run certification courses. If you have the required experience and resources, I would recommend the course from ISB
    • For freshers, there are certification courses run by various institutes, including Analytics Vidhya. Getting these certification courses can increase your chances of getting hired in some of the best companies.

What else can be done?

While these are specific paths for starting a career in Analytics, there are smaller steps you can take to increase your awareness. Some of them are mentioned below:

  • Continue reading on the subject: Keep reading about the subject through various blogs and communities.
  • Attend Industry forums: There are various industry forums, meet and conferences which you can attend to gain understanding of the subject. DataHack Summit (held annually) is one of the places to meet some of the best people in industry
  • Try internships: Internships are awesome way to start your career. They provide you a flavor of the work before you take a plunge and the employer gets to assess you without significant investments.
  • Connect to people: Since it is a small industry today, most of the people know each other. Find out how can you connect and talk to people in industry. They are your best sources of information.
  • Go through training material available on the web: Google provides basic training for Google Analytics free. There are various websites running basic courses which can be accessed freely. Some universities provide access to course notes freely. Consume as much as you can.
  • Books: Read some good books on the subject like Scoring Points (on Tesco); The new Science of retailing, The Signal and The Noise are some good books on the matter.

If there are any other resources which you have found useful to learn more on the subject, please add them in comments below.

Image credit: aibworld.net

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

 

You may also like: Common myths about a career in Business Analytics: Busted!

 

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

Adebiyi Orilabawaye
Adebiyi Orilabawaye

Thanks for this info.Can you forward some soft copies to me to aid my analytic skills.

123

Kunal Jain
Kunal Jain

Hi Adebiyi, Thanks for your interest. Please subscribe to our email distribution list, which will ensure all the articles are delivered to your email address on the day they are published. Thanks, Kunal

123
Harun Murugavel
Harun Murugavel

Hi Kunal, This site is loaded with all the information and answers that's running in back of my mind for which I'm seeking a guidence. This site have all of it compared to the other sites that have come across. Big Kudos to you. But still, i have few clarification. I'm again one of an experienced fresher for this Analytic Industry and decided to step-in with no two ways. After the "Three Mistakes of My Life" 1) 7 Job Changes in 9 years 2) 5 domain Changes 3) Multiple work envionment -Starting from 30,000 member organization to 2000 member to 800 member to 50 Member to 40 Member then finally a work from home job and now back to a 500 member team. With all these I have completely shattered my professional carrier path just for the sake of earning and now I earn jus NUTS if i do a market correction towards my salary. As said, I have madeup my mind for analytics and need your suggestion for 1) How & Where do I start now to reshape my carrier. 2) How does analytics treat maths haters 3) Can I Persue Domain specific Analytics ( E-commerce , Mobile and Media Is my preferred Domain) Please advice. -Harun

123

Kunal Jain
Kunal Jain

Thanks Harun for going through the information on site. Specific answers to your questions: 1) Assuming you have no prior experience in Analytics, you would almost need to start afresh. You should aim to get your foot in industry rather than pushing for higher salary or bigger role at this stage. Openings in MIS / Analytics units of any of the Organizations you have worked in past can be a good way to start your search. 2) If you hate Maths, I would ask you to seriously re-consider a career shift to Analytics. You will live in ocean full of numbers and would need to make sense and stories out of bunch of numbers in unstructured problems. Even though there is no filter for people who don't like Maths, it could be a serious career limiter in the industry 3) Yes, you can. Getting started with web analytics could be the way to go. Kunal

123
Vijay
Vijay

Hi Kunal i need your help in deciding a career shift to analytics. i am 35years and have worked in HR industry for 7 years. now i want to go for analytics career. my GMAT score is 680. will i be accepted at ISB, if not then which is the other best institute. i hear a lot about jigsaw academy, is this a good institute and do they have placemets

123
Vivek Anand
Vivek Anand

Hi Kunal, At first, thank you very much for sharing such useful information. Please continue the same in future as well. Further please suggest that keeping analytics industry in mind what kind of hobby one should develop parallely which could be relevant in having more command towrds this industry and impressive from interview point of view as well. I would like to spend more time on this which could improve my knowledge further for the industry. Regards, Vivek Anand.

123

Kunal Jain
Kunal Jain

Vivek, Your hobby should be based on your interest rather than something which is impressive from interview point of view. Trust me, the decision in interview will not be based on how impressive is your hobby. If you want to prepare for an interview, here are some tips. If you want to develop knowledge about the industry, there is a lot on the internet. Additionally, you can read some of these books. Thanks, Kunal

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