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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

Common myths about a career in Business Analytics: Busted!

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

Some time back, I wrote an article on “How to start a career in Business Analytics?“. The article was well received by people who want to enter Business Analytics. It is still one of the most popular articles on Analytics Vidhya. In response to this article, I received a lot of queries about career in Analytics. While some of them were good queries, some of them were recurring myths.

Hence, I decided to do a follow up article. Not only so, in order to debunk these myths entirely, I decided to publish the articles in all relevant forums.

common myths in analytics career

Here are the myths I received through queries / comments / emails and my take on them:

  • You need to be an engineer to start a career in Business Analytics: The truth is that you don’t. All you need is ability to think structurally and comfort with number crunching. As long as you can put structure to unstructured problems and perform back of the envelope calculations, you are as good as any analyst out there.

Having said that, companies prefer people from quantitative background as they are expected to be better with numbers. By quantitative background, I mean people from any of these disciplines: Engineering, Economics, Maths, Statistics, Physics or MBAs with graduation in these fields.

  • Analytics is about working with large datasets / Companies work with big data day in and day out: This rosy picture is far from reality in most of the Organizations. Experts estimate penetration of big data to be in low single digit percentage among Organizations. Most of the time analytics team work on specific problems, which may or may not involve large datasets. The requirement of the role is to be able to put structure across unstructured problems and be able to use numbers to understand business and the changes required in strategy.
  • You need to be a programmer: I was a good C++ programmer when I started my career in Analytics. Sadly, none of those skills have been utilized in last 7 years and might not be utilized in future. You only need to learn programming for the tool you use for your analysis (e.g. SAS, R, SQL etc.), but you don’t need to be a good programmer before hand to learn these. Also, most of these tools have a Graphical User Interface (GUI), which you can start using with out knowing programming.
  • Learning Analytics is all about learning a tool (SAS / SPSS / other tool): A tool is just a tool to perform Analysis. It can not perform analysis on its own. You need to understand the fundamentals required for performing analysis like:
    • What are the things you need to keep in mind while performing Regression?
    • What can you infer from the coefficients and outcome of t-tests?
    • How do you prove or dis-prove a business hypothesis?

Once you understand these, applying them through any tool can help you start your journey of Analytics.

  • Its difficult to find a job – In fact it is the other way around. Analytics industry is struggling with attrition and shortage of talent. 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.” If you have the right skills, you will be highly sought after (at least in the current market conditions).

Are there any other myths that you are aware of? In case you are, or are unsure whether it is a myth or a fact, please add it below.

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

santosh shriyan
santosh shriyan

Useful article Kunal. Needed to know your views on the depth of statistics knowledge a person should know to make good sense of BA ( you may mention bare minimum and desirable ) .....regs/santosh

123

Kunal Jain
Kunal Jain

Santosh, I would say that at the very least, you need to understand all the basic terms (mean, median, standard deviation, confidence interval, normal distribution, binalmial distribution) and hypothesis testing (Null hypothesis, alternate hypothesis, t-test etc.). You may need to understand lift curve and a few co-efficients, if you want to make sense of predictive modeling. Hope this helps. Thanks, Kunal

123
Mruthyunjaya Rao Padamata
Mruthyunjaya Rao Padamata

Kunal, Do I need a background of finance and current affairs to become a analyst , if not where are those required? Thanks, Mruthyunjay

123

Kunal Jain
Kunal Jain

Mruthyunjaya, Background in Finance is not a critical need, but people from Finance background can become analysts due to quant exposure. Current affairs has no relation with becoming an analyst. Hope this answers your question. Thanks, Kunal

123
Vikesh Bhartee
Vikesh Bhartee

Kunal how is the job scenario for analytics in European countries such as Ireland?? ,/Vikesh

123

Kunal Jain
Kunal Jain

Vikesh, Overall, the European markets are still recovering from recession and there have been some signs of recovery. The demand for Analytics is good, but I am not sure it is good enough to plan a move to these countries. If you have some opportunity in hand, then it is a different matter. You can refer to efinancialcareers.com for job postings in Financial space in UK. If there is some other reason for the query, let me know. I'll help in whatever manner I can. Thanks, Kunal

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