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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

9 Tips for a Seamless Transition to Data Science for Absolute Beginners!

Abhiraj Suresh
Abhiraj Suresh Last Updated : 02 May, 2023
6 min read

Are you looking for a career transition to data science? Wondering how to navigate through the numerous obstacles ahead? The demand for data scientists has been on the rise and it is really important for beginners to start on the right note and the right plan. In this article, we will go through 9 of the top tips, we have for absolute beginners that will help you navigate through this obstacle-filled career path better and efficiently and begin the transition to data science effortlessly.

Table of contents

1. Understand if You Can Become a Data Scientist

Data Science is not everyone’s cup of tea. It is a culmination of various skills and preferences. The question- ” How to become a data scientist?” is a function of learning path, planning, and consistency. The real question that needs answering is should you become a data scientist or not.

So to calm your impulses and think practically, ask yourself the following questions and determine if data science is meant for you or not-

  • Do you love number crunching and logical problem solving – i.e. puzzles, probabilities, and statistics?
  • Do you enjoy working/handling unstructured problems?
  • Do you enjoy deep research and can spend hours slicing and dicing data?
  • Do you enjoy building and presenting evidence-based stories?
  • Do you always find yourself questioning people’s assumptions and are always curious to know ‘Why”?
  • Do you enjoy problem-solving and thrive on intellectual challenges?

The depth of the responses to these questions will help you understand whether to enter the realm of data science or not.

2. Understand the Career Opportunities in the Data Industry

A lot of folks entering the industry get confused amongst the various job roles in the data industry and think of it as a part of data science. Comprising of diverse and interrelated job roles, it is pretty easy to slip into this confusion and derail yourself from your actual career option.

Also Read: Top 8 Job Roles in Data Science Industry That You Should Know!

3. Plan your Learning Journey

Charting a path for yourself is really important to become a data scientist. And staying on the path is even more important. Many people get stuck here. They are just not able to understand to plan it ahead.

The  Certified AI & ML BlackBelt Plus Program program does the charting for you. Its intense 18-month structured roadmap has been built by industry experts with relevant experience to make sure that you get a reasonable time to understand a concept and proceed at your pace.

4. Don’t Get Derailed by Ph.D.

No! You do not need a Ph.D. to become a data scientist!

To understand this, let’s broadly divide the role of a data scientist into two categories:

  • Applied Data Science Role
  • Research Role

It’s important to understand the distinction between these two roles. Applied Data Science is primarily about working with existing algorithms and understanding how they work. In other words, it’s all about applying these techniques in your project. You DO NOT need a Ph.D. for this role.

Most folks fit into the above category. Most of the openings and job descriptions you see or hear about are for these roles.

But if you are interested in Research, then yes, you might need a Ph.D. Creating new algorithms from scratch, researching them, writing scientific papers, etc. – these fit a Ph.D. candidate’s mindset. It also helps if the Ph.D. adds to the domain you want to work in. For example, a Ph.D. in linguistics will be immensely helpful for a career in NLP.

5. Should I Get a Certification?

Well, there is not a definite answer as there are many ways to become a data scientist.

In simple words, certifications definitely matter but because of the skills you have gained as part of the certification and not because of the certificate itself.

Over the decade, many data science certification courses have popped up leading to the generalization of these courses. Now anyone can just take up a certification and claim to be a data scientist but it doesn’t work like that when you apply for a job.

The thing to note here is, recruiters weigh your projects and skillset more than the certificate earned.

In the end, it all boils down to the interview process. The interviewer will test you from each angle possible. So make sure you practice as many projects as possible and have good clarity over the fundamentals.

Now if you go for a certification make sure you take into account the factors like- Time, Skills Taught, Mentors, Prerequisites, Mentors, and cost.

The Certified AI & ML BlackBelt Plus Program program is one of the unique courses that stress foundational understanding. The course includes 100+ hours of live classes and weekly to 1:1 mentorship sessions to make sure you understand everything easily.

6. Be Good at Programming

With multiple languages in the frame, it is really important to be a good programmer in at least one language.

If python is your language, then to be a good enough data science professional in this vast space, you must be well-practiced with base Python and its operations, its basic machine learning libraries like Pandas, NumPy, Scikit Learn.

Also, you should be able to smoothly write custom functions, generators, and so on. Even if you can’t optimize your code at this stage that is fine. You should be able to transform your well-thought operations into a code.

You don’t need to master all the languages but choose one and master it over time. If you believe that you want a holistic view of data science languages and tools you can check out the Certified AI & ML BlackBelt Plus Program program where machine learning experts teach you Excel, SQL, Python, and its libraries from simple Pandas to advanced Keras!

7. Make sure your Statistics is strong

Statistics is the grammar of Data Science.

Yes, Yes, Yes!!! You need to know statistics in order to land a data science job.

But don’t be afraid. You do not need to have a background in Statistics. But make sure the statistical topics relevant to data science is at your tips as it is a part of the foundational knowledge. Some of these topics include.

  • Descriptive Statistics (mean, median, mode, variance, standard deviation)
  • Inferential Statistics (hypothesis testing,  z test, t-test, significance level, p-value)
  • Statistical analysis (linear regression, forecasting, logistic regression)

This is a rough and basic list of topics that you must master and this won’t take much of your time if you find the right resources.

8. Start Participating in Hackathons

Data science is more about practical intuition than theoretical understanding. You need to have a notion of the best algorithm, the best data cleaning technique, etc once see the data irrespective of whether you have an in-depth theoretical understanding or not. And Hackathons help you build this intuition.

Data Science Hackathons are an excellent stepping stone in your data science journey. You get to practice your skills on a dataset, showcase it to the world, and even stand a chance to win prizes.

These hackathons and competitions have increased multi-fold in the last few years as more and more people want a piece of the data science cake. Most aspiring data science professionals include these competitions in their resumes.

Make Kaggle and other hackathon platforms your permanent playground. Also, do have a look at Analytics Vidhya’s hackathon platform- DataHack

9. Hone the Necessary Soft Skills

soft skills data science transition

You cannot be more wrong if you think making a model analyze and predict the future is more than enough for you to become a top-notch data scientist. There are several soft skills surrounding this domain that needs to be honed for effective model building and usage across departments.

Let’s have a look at some of these soft skills-

  • Communication Skills – We just cannot stress enough how important this skill is. The insights from your model need to be communicated effectively to all the stakeholders and a raw data science model cannot be used for communicating with non-technical people involved in business decision making.
  • Storytelling Skills – The way you communicate the insights from data also determines your ability as a data scientist. One of the examples of good storytelling skills is to depict the per-day box office collection of a movie for a bar graph and not in detailed numerical form. The former one just makes it easy to analyze when the ticket sales soared and when they dropped.
  • Structured Thinking – Thinking of the problem from myriad perspectives and breaking down the problem is any data scientist’s prized possession. It helps data scientists factor in the relevant thoughts about the data and the objective from multiple viewpoints.
  • Curiosity – You as a data scientist are required to keep on asking questions. Questions about which algorithms, the problem, ultimate objective from the standpoint of various stakeholders, etc. This will enhance your understanding of the problem and help you better chart the model.

Data Science Roadmap 2023

Take Your First Step to Data Science Transition

In this article, we have discussed 9 of the most important tips that will seamlessly help you transition to data science. Hope this article clears some of your doubts. You can find Certified AI & ML BlackBelt Plus Program as one of the most unique courses that have practically incorporated all the above tips and serve as a single stop to help you become a data scientist.

Here are links to some additional resources that will enhance every beginner’s understanding of the data science spectrum:

In no way does this article suggest that the list of questions is exhaustive. Feel free to comment below any other data science transition tips that you think will help the community.

Blackbelt accelerateData Science Transition
Abhiraj Suresh
Abhiraj Suresh

My name is Abhiraj. I am currently a manager for the Instruction Design team at Analytics Vidhya. My interests include badminton, voracious reading, and meeting new people. On a daily basis I love learning new things and spreading my knowledge.

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Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
10 Questions Every Data Science Beginner Asks (with Answers and Resources)
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