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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

All you need to know to start a career in analytics

Tavish Srivastava
Tavish Srivastava Last Updated : 04 Nov, 2024
3 min read

AV blogsIn last two years, we have successfully tried creating a community dedicated to share best practices across industry and give a kick start to all newbies. We received unconditional support from the people in the industry beyond the boundaries of experience and geography. If you search analyticsvidhya.com for any relevant topic of analytics, there is a good possibility you will find articles talking about the topic with different angles. This article will serve as an index to all the topics required to get a broad level exposure to the industry. Had I been new to the industry, reading the articles mentioned in this blog would have given me just the knowledge I would need to understand the industry:

Categories of articles published

Broadly most of the articles published on AnalyticsVidhya can be classified into 5 categories :

1. Analytics Interview related articles

2. Analytics Tools related article

3. Articles explaining popular predictive/descriptive algorithms

4. Analytical framework related articles

5. Other Interesting articles

For a fresher, I will recommend to go through each and every article talking about the first four categories. The articles have been designed in such a way that it strikes just the right balance of ease to interpret and knowledge depth covered.

These articles will broadly cover

1. Tips and tricks to ace an analytics interview

2. Commonly asked Case studies

3. What is the interviewer looking for in a candidate?

Following is how AV will prepare you to crack analytics interview :

1. Start your preparation with this article https://www.analyticsvidhya.com/blog/2014/07/definitive-guide-prepare-analytics-interview/

2. Initiate solving puzzles with https://www.analyticsvidhya.com/blog/2014/09/commonly-asked-puzzles-analytics-interviews/

3. Learn to solve Guess Estimate (one of the most common interview question) with https://www.analyticsvidhya.com/blog/2014/01/tips-crack-guess-estimate-case-study/

4. Start solving business case with 3 article series starting https://www.analyticsvidhya.com/blog/2014/02/learn-analytics-business-case/

5. Try solving more complex business case studies with https://www.analyticsvidhya.com/blog/2014/02/interesting-analytics-case-study/

6. Start solving data handling tools with

a. https://www.analyticsvidhya.com/blog/2013/11/4-sas-tricky-analytics-interview/

b. https://www.analyticsvidhya.com/blog/2014/05/tricky-interview-questions/

c. https://www.analyticsvidhya.com/blog/2014/04/tricky-base-sas-interview-questions-part-ii/

Analytics Tools related article

Following are some articles (not a complete list) on AV on this subject :

1. SAS Based articles :

a. https://www.analyticsvidhya.com/blog/2014/06/sas-launches-free-version-but-good-enough/

b. https://www.analyticsvidhya.com/blog/2014/10/introduction-sas-macros-conditional-iterative-statements/

c. https://www.analyticsvidhya.com/blog/2014/10/introduction-sas-macro/

2. Python Based articles

a. https://www.analyticsvidhya.com/blog/2014/09/data-munging-python-using-pandas-baby-steps-python/

b. https://www.analyticsvidhya.com/blog/2014/09/mining-youtube-python-social-media-analysis/

3. Qlikview related articles

a. https://www.analyticsvidhya.com/blog/2014/09/qlikview-incremental-load/

b. https://www.analyticsvidhya.com/blog/2014/08/qvds-qlikview-application-efficient/

 

And here is an article to test your expertise on various data handling tools https://www.analyticsvidhya.com/blog/2014/09/test-level-expertise-sasrpython/

Predictive Algorithm related article

Following are some articles (not a complete list) on AV on this subject :

1. https://www.analyticsvidhya.com/blog/2013/12/extracting-variables-regression-model-2/

2. https://www.analyticsvidhya.com/blog/2013/11/getting-clustering-right/

3. https://www.analyticsvidhya.com/blog/2014/10/introduction-neural-network-simplified/

4. https://www.analyticsvidhya.com/blog/2014/08/visualizing-market-basket-analysis/

5. https://www.analyticsvidhya.com/blog/2014/07/interpret-hidden-state-latent-markov-model/

6. https://www.analyticsvidhya.com/blog/2014/10/introduction-k-neighbours-algorithm-clustering/

7. https://www.analyticsvidhya.com/blog/2014/10/support-vector-machine-simplified/

8. https://www.analyticsvidhya.com/blog/2014/07/markov-chain-simplified/

9. https://www.analyticsvidhya.com/blog/2014/06/comparing-random-forest-simple-cart-model/

10. https://www.analyticsvidhya.com/blog/2014/06/comparing-cart-random-forest-1/

11. https://www.analyticsvidhya.com/blog/2014/06/introduction-random-forest-simplified/

12. https://www.analyticsvidhya.com/blog/2014/04/solving-survival-model/

13. https://www.analyticsvidhya.com/blog/2014/04/survival-analysis-model-you/

Here comes the most important group of articles published on AV. These articles explain who can become an analyst, how to become an analyst and how to sharpen your analytical thinking. Following are some articles (not a complete list) on AV on this subject :

1. https://www.analyticsvidhya.com/blog/2014/09/become-data-scientist-business-analyst/

2. https://www.analyticsvidhya.com/blog/2014/01/train-mind-analytical-thinking/

Other Interesting articles

Other than these subjects we have also published some articles on interesting topics. These articles not only deepen your understanding of tools and statistics but also increases your excitement for the field of analytics. Following are some articles (not a complete list) on AV on this subject :

1. https://www.analyticsvidhya.com/blog/2014/07/facebook-analyst/

2. https://www.analyticsvidhya.com/blog/2014/07/world-cheering-2014-fifa-wc-winner-twitter/

3. https://www.analyticsvidhya.com/blog/2014/05/build-word-cloud-text-mining-tools/

4. https://www.analyticsvidhya.com/blog/2014/04/probability-action-monty-halls-money-show/

 End Notes

We have tried covering a diverse group of topics related to analytics on AV. However, any suggestions for genre of articles or specific articles is most welcomed. Share with us new topics or commonly faced concern in the industry to be covered on our website.

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.

Tavish Srivastava
Tavish Srivastava

Tavish Srivastava, co-founder and Chief Strategy Officer of Analytics Vidhya, is an IIT Madras graduate and a passionate data-science professional with 8+ years of diverse experience in markets including the US, India and Singapore, domains including Digital Acquisitions, Customer Servicing and Customer Management, and industry including Retail Banking, Credit Cards and Insurance. He is fascinated by the idea of artificial intelligence inspired by human intelligence and enjoys every discussion, theory or even movie related to this idea.

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Responses From Readers

Clear

Anon
Anon

Very useful. Thanks.

123

Mohammed Wasim
Mohammed Wasim

Hi Kunaal, I have been a regular reader of your website. The article written is very very useful. Kudos to you for all your efforts :) Cheers, Mohammed

123

Santanu
Santanu

Hi Kunal I am a Insurance professional having career experience of 7yrs in sales and business development at SBILIfe,Bancassurance. I am BE (automobile) and MBA (marketing). I am exploring opportunities in Business Analytics.I have already completed my training in SAS, VBA, Excel,R,Ms Access at IVY, kolkata.But not getting any calls.Already registered myself in naukri also. Could you please guide me. Thanks Santanu

123

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