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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

Why Business Analytics Degrees Aren’t Just a Fad

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Guest Blog Last Updated : 24 Sep, 2015
4 min read

Introduction

Data can be defined as a collection of information, used to derive useful insights for informed decision making. Right from analyzing traffic patterns on a small business website to determining strategies for conversion optimization, to more complex industrial algorithms that determine how much laundry detergent a big box retailer should deliver to an individual location; data is playing a vital role in determining B2C or B2B insights and thereby formulating business strategies. The usage of data is becoming more and more mainstream in today’s world.

business analytics, analytics vidhya

 

More than 90 percent of the data that exists today has been created since 2011 — and thanks to our technologically driven society. Billions of bytes of new data are created every day. But what do we do with all of that data?

Without interpretation or context, it’s all but useless. Without knowing what to pay attention to, and what it all means in the larger scheme of things, big data is nothing more than a big pile of information.

That’s where ‘Business Analyst’ comes in. Business Analyst helps in making sense out of raw data.

 

 

Why do companies require Business Analysts?

Back in 2012, the Harvard Business Review called the position of data scientist the “sexiest” job of the century. That description stemmed from the fact that in the latter half of the first decade of the oughts, the notion of big data was only beginning to gain notice. Online businesses, especially social media companies, were collecting reams of data about their users, but weren’t sure how to use it to their advantage.

analytics vidhya, business analytics  Analysts began asking questions, looking for patterns, interpreting      data, and developing insights that could be used to grow their  businesses. Based on the success of companies like LinkedIn, which  relied heavily on analytics to develop the features that attracted  millions of new users (and subsequent revenue growth) other  companies began looking to scientists who could make sense of their  data, and more importantly, show them how to use it.

The HBR called the job of data scientist — which has since morphed into business analyst in many cases — sexy, and the employment picture agrees. According to the U.S. Bureau of Labor Statistics, demand for business analysts is expected to increase by 25 percent by 2020. In short, everybody wants one — but not everyone can get one.

According to a report on big data from research firm McKinsey & Company, there could be a shortage of nearly 200,000 analysts who have the in-depth skills necessary to interpret big data by 2018. Perhaps even more alarming is the same report predicts a shortage of 1.5 million managers or analysts who know how to actually use the insights gathered from big data to make effective decisions.

 

What does it take to become a Business Analyst?

The Bureau of Labor Statistics notes that most business analysts have a degree in a computer or technology-related field with additional training in business. However, the most successful candidates have a master’s degree, which combines their deep knowledge of the computer systems required for gathering and interpreting data with the business expertise necessary for applying it.

Colleges and universities across the country are meeting the demand for this specialized knowledge with comprehensive programs that focus on gathering, analyzing and prioritizing business and system data and requirements, and preparing students for work in this field.

Some claim, though, that the focus on big data is “just a fad,” and investing time and energy into business analyst education will prove unnecessary. However, the facts suggest otherwise, and there are more than a few reasons that a business analysis degree is a good investment:

  1. Job Prospects. Not only are qualified business analysts in high demand, the starting salary for thesebusiness analytics, analytics vidhya, business analyst positions is competitive. The BLS reports that the median annual wage for an analyst is about $79,000; starting salaries tend to run in the low $50,000 range.
  2. Transferrable Skills. Skills in business analytics are not generally industry specific. Business analysts are in demand across verticals. Some experts consider this applicability across industries to be a boon to innovation; analysts can bring their insights and ideas from seemingly disparate industries together to create new, exciting, and unique products.
  3. New Tools and Techniques. Statistical tool companies, like SAS, SPSS, R, Python etc are constantly developing new tools and technologies for gathering and interpreting data. However, most of these tools are only as good as the people using them, creating a high demand for seasoned professionals.
  4. Data Isn’t Decreasing. The fact is, big data is here to stay, and it’s only going to get bigger. Thanks to cloud computing, social media, security concerns, the Internet of Things, and several other trends, the amount of data that needs interpretation and application will only grow. The people who know how to extract the important stuff from that data are the ones who will be in demand.

Clearly, then, earning a degree in business analytics is a solid investment in your career. Developing not only the technical skills for data collection and analysis, but knowing how to think about the data and interpret it correctly will prove valuable going forward.

 

End Notes

In this article, we discussed about the importance on business analytics in an enormously populated world of data. We also looked at how business analysts are best at playing with data, thus helping businesses globally to make informed decision making. This article also discusses the job demand of business analyst in the near future.

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.

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Guest Blog

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