Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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

MyStory: How I became a Data Science Analyst from a Software developer?

G
Guest Blog Last Updated : 18 Feb, 2019
6 min read

 Background

Don’t let the noise of others’ opinions drown out your own inner voice.

-Steve Jobs

To be honest, my inner voice always told me to believe I am good at numbers & communication, and no matter how many wrong paths I took, my boat sailed all the way to the shore I was meant to be on.

Before I reveal how I got introduced to this phenomenal field –Data Science & Analytics, I will take you through what other jobs I tried my hands on. Moreover, I wouldn’t say I have reached the pinnacle of success, but I am sure I have secured a right track at least for now.

Well, my journey till here is filled with lot ups and down. Read my full story to know more.

 

How it all started?

I was in my pre-final year at college, I honestly had no clue what profession I am going to be in after I finish my graduation (like most Indian students). I bagged an internship at a startup as “Trainee Android developer”.

Well to be honest, I did like the idea of creating stuff, but I never had that thing for core software development. The only thing that carried me to take this internship was my passion for creating the beautiful user interface(s). I did go through some MOOCs on android development before taking up the internship. After 2 months of creating apps, I started to get the feeling that it was not my thing. Well, that definitely doesn’t imply that I was not doing well at it, but I just could not see myself doing that for the rest of my life.

Quick Take Away: Finding your passion in life is essential.

 

Getting the Flavour

It was early in 2015 when I got a flavor of this amazing field. As I recall, it was after one of those popular questions all aspiring data scientist ask “Which programming language for Data Science- R vs Python vs SAS?”

The best answer to this question could be found nowhere else but on this phenomenal website – Analytics Vidhya (AV). That’s how I got introduced to AV. From that day onwards I have been spending a significant time of my life on AV. But the real challenge was yet to show up.

Did I grab a job in analytics yet?

NO! So it was still not a merry time for me.

Quick Take Away: The journey starts with one thing, no matter how long or short.

 

The Struggle is for real and is always worth it

I started to apply for data science jobs, but most of the positions sought Masters / PhD students (especially in statistics). And besides that, I also started looking for predictive business analytics courses [classroom], as I was out of college by this time.

Learning from MOOCs is not easy and is time-consuming especially when you have a job in a totally different field. I must admit perseverance was the key that got me through it. I kept following AV and started attending most of the Data Hackathons they conducted especially offline ones. It was one great place to meet people of same mindset, which allowed me to learn most tricks for data science.

I also picked up some basic courses on Coursera, Excel to MySQL techniques for business is one. Initially, I use to score miserably in competitions but I just kept on trying. Remember, even if you win the competition or not, you are always taking away good amount of knowledge – so keep on participating in competitions. In my case, I realized I was the kind of person who performs the best if there is a deadline associated with it.

Meanwhile, I was working as an Analyst under Property & Casualty vertical of Insurance. My nature of work didn’t require me to use much of the cutting edge technologies. In spite of that, I always tried looking for ways to use new tools and techniques in order to improve the way I did my job, which of course allowed me to learn a lot.

One thing to my readers -“No matter how difficult the path to your dream seems, just getting back at it again & again and yet again makes it easier than ever”.

I am more than thankful to Analytics Vidhya for laying out a path via ”The Ultimate Plan to Become a Data Scientist in 2016”. By sticking to this path it can land you into wonders, trust me.

Word of caution: Data Science & Analytics is a research oriented field don’t just pursue it because there is a lot of hype behind it. If you don’t like exploring your brains off on a particular case / topic in order to yield few but meaningful information, sorry to say this field is not meant for you.

 

The Win – Win Situation

Like they say, it’s 10% luck, 20% skill, 15% concentrated willpower, 5% pleasure, 50% pain and a 100% reason to win the game. Well, this shouldn’t be taken literally but yeah a typical success story comprises of these.

The few things that I realized defines your success in this field are:

1.) Attitude 

2.) Aptitude

3.) Discretionary Efforts

4.) Communication Skills (is like bread and butter)

5.) Tools and Techniques (must have).

Having said that, I choose my way to success by being hell-bent to learn all it takes to be in decision engineering (Data Sciences). In the span of 8 months of my job as an Analyst, I practiced on different data science tools at my workplace.

The skills which I learned on my way allowed me to relate the conceptual problems into pragmatic approach of solving the problems. Well, to some extent 10% of my luck also played an important part in landing me a Data Science job.

Fortunately, later that year my organization declared a new business vertical- Data Science.  After finding out this I was the happiest person in the organization (I bet there were many more though). Every discretionary effort I made until now determined the probability of my inclusion in this Business vertical. I applied for the position with a portfolio of all my projects by my side. In addition to this, I coordinated the introduction session to Data Science vertical which allowed me to closely meet the Business unit head that later led us into further interaction(s).

My Portfolio of projects, AV-Data Hack Meetup Winner Certificate, Technical Skills and Discretionary Efforts (like I have mentioned before) made me nail this position. Working as a data science analyst requires one to be a quick learner, every new tool brought into the arsenal should be grasped really quickly. It’s quite incumbent to have

It’s quite incumbent to have an insatiable passion for learning new things if you want to be a successful data scientist. I have not achieved success yet, but I am pretty sure I have embarked on the right path. After all, the only thing that determines the ultimate success is nothing but perseverance.

My Advice to People

Thinking like an entrepreneur really helps, a successful data scientist shouldn’t restrict themselves to only building models. One should also find opportunities to get involved in other vital roles of Analysis process.

Tools and techniques are important, but only being an adept at using them won’t lead to ultimate success. Both hypothesis generation and data preparation are equally important. One who is not able to communicate the insights drawn from the analysis process would never fall in the category of Data Science unicorns.

 

Which is more important for a Data Analyst: Intellectual Curiosity/Intuition V.S Deep Statistical Knowledge?

Intellectual curiosity is the most important thing. The understanding of statistics can be gained, but the curiosity is more innate, if you’re not naturally into working with data, you can be a fantastic analyst, but you become a specific type of analyst. These analysts may not care enough to build a better model.

They lack creativity and the determination to go the extra mile. They’ll need hand holding and will struggle to resolve new problems. But being curious to know more insights about the data you can overcome the lack of statistical knowledge.

 

End Notes

People willing to pursue a successful career in this phenomenal field should be inclined towards “Using data science than doing data science”. Essentially, one should not only be good at providing solutions to problems at hand, but also be able to identify data problems and take accountability of “using” data science to structurally solve these problems. An analyst is only as good as their ability to independently work on problems. So, break free from whatever is holding you back.

 

About the Author

  • Ankit Dwivedi is data science aficionado & computer science engineer with 3+ years of experience as a Data Analyst in the Data Science & Analytics vertical at Wenso Ltd. (a leading IT firm in UK). Wenso, being a paramount in GDPR consultation in the UK, deals with a lot of EU data, bringing in the need for better Data Science solutions. Here, he is working on a gamut of Data Science problems. In his free time, he likes reading articles & solving problems in competitions at Analytics Vidhya and Kaggle.

    Ankit is the winner of What’s Your Story. We found his story very inspiring and that has earned him Rank 1 in the competition. To read the stories of other winners. Stay Tuned!

Disclaimer: Our stories are published as narrated by the community members. They do not represent Analytics Vidhya’s view on any product / services / curriculum.

Learn, compete, hack and get hired!

analytics success storiesAV storiescareer guidancecareer in data sciencecareer shiftdata science analystdata science careersIT to data sciencesoftware developerSoftware developer career movesuccess storiessuccessful stories
G
Guest Blog

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Soham Lawar
Soham Lawar

Well said Ankit! Thanks for sharing your thoughts!

123

Ankit
Ankit

Thank You! Soham.

123
Ankit Dwivedi
Ankit Dwivedi

Thank you!

123
SHRUTI
SHRUTI

Nice article Ankit!! Truly said-In this field of Data Science, Intellectual Curiosity is important to always go an extra-mile...:-) Thank you for sharing your story. Thank you AV!!

123

Ankit Dwivedi
Ankit Dwivedi

Thank you! Shruti.

123
Arun Kumar Sharma
Arun Kumar Sharma

Great article for those who are looking for employment in data science. I am interested in consulting work or starting my own analytics startup. Kindly share the stories of freelance consultant working in data science domain.

123

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details