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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 can a Statistician Become a Data Scientist?

Yana Khare
Yana Khare Last Updated : 12 Nov, 2024
7 min read

Introduction

Statistics can be traced back to the mid-18th century, while Data Science is a relatively new concept. The term Data science was created in the 1960s and is deeply rooted in statistics but now has evolved into artificial intelligence, machine learning, etc.

Data science has seen drastic growth due to the exponential development of the internet. The data science industry is constantly evolving as more and more businesses seek to increase their sales and profits. This makes it both rewarding and demanding for its practitioners to constantly evolve and come up with new ideas and ways to collect, store and analyze data.

Due to the high demand for data scientists, the demand for statisticians has significantly reduced. And if you are looking to change your career from a statistician to a data scientist, you have reached the right place.

I hope that by the end of this article, you will learn the similarities and differences between a data scientist and a statistician, what additional skills you would be required to learn as a data scientist, and which courses you could take to become a successful data scientist.

In the true words of a “know-it-all” Mr. Sherlock Holmes, “It is a capital mistake to theorize before one has data.”

Let’s gather all the data before you take the necessary steps required to become a data scientist.

Statistics

Before dwelling further, let’s look at the differences and similarities between a statistician and a data scientist.

Table of Contents

Similarities Between a Statistician and a Data Scientist

People often say that there is a vast difference between a Data Scientist and a Statistician, that is true, but there are also a few similarities between the two.

  1. Strong Statistical Skills: Both data scientists and statisticians have a deep understanding of statistical concepts and methods. They use a combination of mathematical, statistical, and software skills to identify patterns, relationships, and trends in data.
  2. Proficiency in Programming: Both data scientists and statisticians need to be proficient in programming languages like R, Python, or SAS. They use these languages to manipulate and analyze data.
  3. Expertise in Data Modeling: Both data scientists and statisticians have expertise in data modeling, which involves developing mathematical models to describe the relationships between variables in a dataset.
  4. Attention to Detail: Both data scientists and statisticians require attention to detail in their work, as they need to ensure that their data is accurate and their analyses are appropriate.
  5. Problem-solving Skills: Both data scientists and statisticians need to be able to solve complex problems and develop innovative solutions to data-related challenges.
  6. Data Extraction Technique: Both data scientists and statisticians design and implement data extraction techniques to locate and obtain useful data and thus share various skills, including the ability to find data, employ analysis, understand trends, and create visualizations.
  7. Same Goal: Both data scientists and statisticians work closely with data to better understand the past and the present and based on those, they make predictions about the future which would lead to a company’s success.

Now that we have had a look at the similarities, let’s look at the differences now.

"Data Scientist

Differences Between a Statistician and a Data Scientist

Being a Data Scientist and a Statistician is like comparing apples and oranges. So, let’s compare them.

           Parameters             Statistician             Data Scientist
Traditional vs. Modern Statistics, with its well-established theories and more traditional roots, has its applications rooted in smaller-scale methods of data collection like surveys, polls, etc.  Data Science, with its more digitized and globalized roots, collects huge amounts of data from various sources such as social media monitoring, online tracking, recommendations, etc.
Usage of Data Statisticians use the data collected to provide insights and explanations for a specific phenomenon. Data scientists use the collected data to predict the outcomes of a certain phenomenon.
What they work with Statisticians typically work with numerical variables and categorical variables, so the information that can be stored is small. Data Scientists work with varied variables like text, voice, image, video, etc., which take up astronomical space.
Time Consumption Statisticians are provided with tidy data, so they typically spend the majority of their time constructing models rather than cleaning data. Data Scientists are provided with imperfect data and have to spend a considerable amount of time preprocessing and wrangling the data before feeding them into the model.
Methods and Tools Statisticians use statistical methods like hypothesis testing, deviation, inferential analysis, etc, to analyze and interpret data. Data scientists use a variety of tools and techniques, including machine learning algorithms, statistical analysis, and data visualization, to analyze and interpret data.
Models Statisticians rather work to improve a single, simple model to best fit the data. Data scientists focus on comparing several different methods to create the best machine-learning model.
Educational Background Statisticians usually have a mathematics background. Data scientists tend to come from engineering backgrounds.
Business Acumen Statisticians may focus more on technical analysis. Data Scientists are expected to have business insight.

Just to elucidate, there may be times when a data scientist would need help with certain data sets from someone with a background in statistics, and at the same time, a statistician may also need help from a data scientist when they have huge amounts of data with many different variables.

Having a strong foundation in statistical knowledge is always a plus point!

Types of Data Scientists

Before kickstarting your career, let’s get to know about what all types of data scientists are there.

Data science has invaded almost all domains of life, from the e-commerce industry to the manufacturing industry to the healthcare industry. With the growing popularity of data science, job roles suited to the various domains have also emerged, such as actuarial scientist, business analyst, data engineer, data mining expert, data quality analyst, cloud computing expert, software programming analyst, and many more.

For a Data Scientist, your career would look something like this-

"Data Scientist

Additional Skills Required to Become a Data Scientist

In addition to your statistical skills, to become a successful data scientist, you would need to have the following skills-

  1. Programming Skills: One must know Python, R, and SQL programming languages to have a better command, as these are the most popular and well-used programming languages for data science.
  2. Business Knowledge: A good understanding of business strategy will be an integral part of the job to becoming a skilled Data Scientist.
  3. Cloud Computing: Unlike how Statisticians are used to storing their data on their computer, as a Data Scientist, you would need to store your data on the cloud. Thus, having an understanding of the basic principles of how it works can be a useful skill for data scientists.
  4. Out-of-the-box Thinking: As a Data scientist, you would need to be open to new ways of thinking as it may lead to new insights.

Courses to Take to Become a Data Scientist

To get an edge over other Data Scientists, one must consider doing the following courses-

  1. IIT Madras CCE offers Advanced Data Science Certification over the duration of 7 months at Rs 5,500/- per month, where an individual will learn how to master all the necessary skills required, like Machine Learning, Deep Learning, Artificial Intelligence, etc.
  2. Introduction to AI and ML by Analytics Vidhya
  3. IIM Kozhikode also offers Certification Course in Data Science for Rs 71,750 over the period of 12 weeks for professionals from any domain looking to advance their careers.
  4. Analytics Vidhya also offers AI & ML Blackbelt Plus Program, where you get one on one mentorship, learn industry-level skills, 50+ real-world projects, dedicated interview preparation, and placement support at just Rs 64,999/-
  5. IBM also offers a Data Science Profession Certificate course to kickstart your career. It is a free 5-month course where you can learn Python, SQL, data science skills, etc.
  6. Harvard University, with its Harvard Data Science Certification program, will teach you the fundamentals of Data Science and statistical concepts and learn how to implement machine learning algorithms. It is a self-paced program at $792.8
  7. The University of Michigan teaches you how to study Python programming language to study Data Science over a period of 32 weeks.

Why are Companies Hiring more Data Scientists and not Statisticians?

Now before considering shifting your career, one must be hoping as to why companies are hiring for data scientists jobs as compared to statisticians. Well, the answer is pretty simple.

  1. In the highly computerized world where everyone is constantly on their phones and laptops, data scientists collect huge amounts of data and with that, they would need the necessary skills required to manage that data, and as we read above, statisticians work with smaller amounts of data and don’t necessarily have the required computing skills.
  2. Data Scientists also need to have statistical knowledge, and thus, companies see this as a two-in-one offer.
  3. It’s a common consensus that data scientists tend to be better programmers than statisticians and thus, making them more efficacious for most organizations, where data is unorganized and messy.
  4. Data scientists have an in-depth knowledge of the business, leading them to be a hot property in today’s world.
  5. Data scientists, to share their findings with business managers, stakeholders, etc need to have strong communication skills
  6. Data scientists can understand their customers at a very granular level which helps the company make policies that are best suited for the customer experience leading to an increase in sales and profits.

The above points can be simplified in this sentence:

"Data Scientist

Conclusion

Data science has seen drastic growth in today’s world and doesn’t seem to stop anytime soon. Take the leap and get the opportunity to learn and grow.

Here are a few more tips you could add to your basket-

  1. Capitalise on online resources- Many resources are available online, like blogs, YouTube videos, ebooks, audiobooks etc can be taken advantage of to further your skills as a data scientist.
  2. Dwell into the data science community- Nothing is better than learning from others who are already in the industry, it can be beneficial to your career and help broaden your horizons.
  3. Networking and Conferences- Getting knowledge online is as important as making connections and attending conferences and events held all the time to learn new things and build professional connections.
  4. Find a mentor- Having a person with knowledge and the know-how to be a data scientist can be immensely helpful.

Hope with this article, you would have learned:

  • The similarities and differences between a Statistician and a Data Scientist.
  • Additional skills and courses you would need to become a successful Data Scientist.
  • Make your decision for a career change.
Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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