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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

Important Job Roles in Data Science Industry Today – Who Does What ?

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Guest Blog Last Updated : 23 May, 2018
4 min read

Introduction

One evening, I was catching up with a friend over a few drinks – let’s call him Jon (name changed). He seemed determined to become a data scientist and was charting out his career plan accordingly.

I quizzed him around his awareness of what a data scientist does and sniffed that he wasn’t sure. So I threw this puzzle to him:

There are 4 people A, B, C and D, each with one of the these designations: A Data Scientist, A Data Engineer, A Data Analyst and a Data Architect. I also had job descriptions of their roles, which I passed on to Jon. He had to match the description to the right designation.

To penalize him for his errors, he had to take an alcohol shot for every wrong match he did. Can you guess the number of shots he took that evening? How many would you end up taking, if you were in this situation?

I would just say that he was lucky that I didn’t throw this puzzle at him with all the 8 designations I had JDs of that evening.

 

The problem

While the demand for various data science roles is increasing by the day, people in industry have used the designations and descriptions a bit loosely. Hence, there is a lot of confusion around who does what in the industry!

We hope this infographic “The Data Science Industry: Who Does What designed by DataCamp” helps you to find out ideal job role. We have provided a brief description of these roles and an infographic for you to take away.

If you have any questions related to designations and roles, feel free to ask them through comments below. I will be as helpful as my time can permit. Enjoy!

 

Description of various data science roles:

The Data Scientist

Data Scientist is most likely one of the most sizzling job titles that you can have these days, and with a yearly average salary of $118,70, they are amongst the top earners in the data science industry. The data scientist has the ability to handle the crude data using the latest technologies and techniques, can perform the necessary analysis, and can present the acquired knowledge to his associates in an informative way.

 

The Data Analyst

Languages like R, Python and SQL are part of the data analyst’s basic knowledge. Much like the data scientist role, a broad skillset is also required for the data analyst role, which combines technical and analytical knowledge with ingenuity. This profile is often looked for by companies such as HP and IBM.

 

The Data Architect

Data is (being collected) everywhere and as a consequence, more and more organizations are in need of a data architect. Industries like banking and FMCG use data architects to integrate, centralize, protect and maintain their data sources. These architects often work with the latest technologies such as Spark and always need to be on top of the game to stay relevant.  

 

The Statistician

The title of statistician is regularly overlooked by or replaced by fancier sounding job titles. This is a bit of a pity, given that the statistician, with his solid foundation in statistical theories and methodologies, can be seen as the pioneer of the data science field. It is often he who reaps the information from the data and transforms it into actionable insights. To end, although the title can sound a bit dull compared to the others in this list, modern statisticians are always ready to rapidly ace new advancements and utilize these to benefit their research.

 

The Database Administrator

As a database administrator, you ensure that the database is accessible to every stakeholder in the organizations, is performing legitimately and that the necessary safety measures are in place to keep the stored data save. You need to master different technologies going from SQL and XML up to a more general programming language like Java.  

 

The Business Analyst

This is probably the least technical profile mentioned on the infographic. However, the business analyst compensates for this lack of technical knowhow with a profound understanding of the various business processes that are in place. A business analyst therefore often performs the role of the middle person between the business folks and the techies. Organizations searching for business analysts are companies like Uber, Dell and Oracle.

 

Data and Analytics Manager

The data and analytics manager steers the direction of the data science team. This individual consolidates strong and specialized skills in a various arrangement of advancements (SQL, R, SAS, … ) with the social aptitudes required to deal with a group. It’s a hard employment but if you feel up for the challenge, make sure to have a look at offerings from companies such as Coursera, Slack. Luckily, with a yearly average salary of $116k, the financial compensation is in line with the high requirements.

 

roles in data science analytics

Author:

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Martijn Theuwissen is co-founder of DataCamp, which helps people learn R and Data Science from their browser and their comfort through fun videos and interactive coding challenges.

 

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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Responses From Readers

Clear

saraswathi
saraswathi

Is this amount correct ? on the second line of this article that yearly avg. salary for the data scientist is 118,70 ?

123

David
David

Thanks for touching on this. My interest is in the difference between Data Scientist and Data Analyst. Is it that the former can handle "bigger" data than the latter using parallel computing, performs predictive modeling rather than just descriptive analysis by the latter, and produces visualizations where the latter might not? Related question, do Data Scientists typically begin their career as a Data Analyst? The reason I ask is that I'm finishing studies in Data Science but rarely see entry level positions with that title.

123

Venkat
Venkat

Hi, Thanks for this wonderful article on the various profiles. I think the avg annual sal for a data scientist should be $118.7K and not $118,70 as mentioned. Regards, Venkat

123

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