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

Want to Build a Career in Data Science? Learn from these 5 Data Science Videos!

Ram Dewani
Ram Dewani Last Updated : 23 Nov, 2020
6 min read

Overview

  • Here is a list of 5 Data Science Career Related Webinars
  • Feel free to add other data science career webinars in the comments that you feel should have made the list

 

Introduction

Since my college days, I had dreamt of becoming a data scientist at a reputed company. The field of data science is lucrative (because of the high salary band, jobs) but I also loved problem-solving, programming, and business domain – I knew Data Science was the field for me. But there were many questions! The first one being –

What do I need to do to become a great data scientist?

meme

I used to ask my seniors, college professors, everyone gave conflicting views. It was definitely tough to navigate through the career from an outside view! I came across a few data science career meetups and attended them. It was one of the best decisions I made. Attending meetups and webinars have their advantage due to several reasons –

  • Get to learn from industry insiders
  • A great opportunity for networking
  • Get career advice and data science knowledge from experts

Meetups are a great way to learn and connect but since Covid-19 that is not possible, Webinars are one of the best alternatives to learn and interact directly with industry experts. In this article, I have given a collection of Top 5 webinars on how to navigate your career in Data Science.

As I mentioned above, it is hard for outsiders to navigate through the data science industry, hundreds of questions pop up – Which language should I choose, which vertical should I go for? and so on. That is why Analytics Vidhya’s AI and ML blackbelt program comes with 1:1 mentorship calls. The expert Data Science mentor will create a customized learning path that suits the career that you want to achieve. It offers 14+ courses, 25+ projects along with expert mentorship sessions! 

Starting a data science career without proper guidance and planning can be confusing. We have compiled a clear-cut free roadmap guide to building a career in Data Science that is curated by expert curators at Analytics Vidhya – 

 

Table of Contents

  1. Data Science vs. Data Engineering – Can you really separate them?
  2. Career Transition Into Data Science
  3. Problem Solving In Business Analytics and Data Science
  4. Going Beyond your First Machine Learning Project
  5. Data Science Careers – Which Role to go for? – Mathangi Sri

1. Data Science vs. Data Engineering – Can you really separate them?

Most aspirants in the data science field want to land the coveted role of a “Data Scientist”. But did you know that tech giants such as Netflix, Facebook, Amazon, etc are hiring Data Engineers like never before to process the massive amount of data they are gathering!

Surprising, isn’t it? On second thought, not really. After all, “A data scientist is only as good as the data he/she has access to.”

This webinar is a great opportunity for you to hear from eminent industry experts who have seen both the data science and data engineering industries up close. Hear and learn from Kunal Jain (Founder & CEO, Analytics Vidhya), Ujjyaini Mitra (Head of Data for Zee5), K. Sankaran (Director, Data Science, LatentView Analytics) and Sachin Arora (Partner and Head of Lighthouse KMPG in India), as they draw upon their experience to help you navigate through these questions.

Key Takeaways:

  • Introduction
  • The spectrum of Data Professions
  • Data Science Vs Data Engineering
  • Roles
  • Responsibilities
  • Applications and Impact
  • Skills
  • What is right for you now “Data Science OR Data Engineering”?

A majority of data aspirants don’t know the number of career fields within the data science domain and those who do are afraid to choose one without proper guidance. That’s why Analytics Vidhya’s AI and ML Blackbelt+ program comes with 1:1 mentorship sessions. These expert mentors customize the learning path according to your career goals so that you are never off-track!

2. Career Transition Into Data Science

Data Science is relatively a new field and a lot of professionals are excited by the opportunity this field provides like high career growth, higher salary band, etc.

However, switching to a career in Data Science has its own challenges for freshers as well as for experienced professionals. Some of the most common challenges faced by Data Science aspirants are:
  • Can non-technical people transition to Data Science?
  • Will an experienced professional be treated as a relative fresher when he/she makes a switch to data science?
  • Which Data Science role should they consider?
  • Will their existing skill-set be useful in Data Science?
  • and more such questions.
To help address all these concerns, Analytics Vidhya & KPMG in India together conducted this webinar on “How to make a successful switch to Data Science”.

Key Takeaways:

  • Who should transition to Data Science?
  • What are the challenges faced while transitioning a career into Data Science?
  • How should freshers prepare for a successful transition into Data Science?
  • How should working professionals prepare for a successful switch into Data Science?

 

3. Problem-Solving In Business Analytics and Data Science

Structured Thinking (problem-solving) is one of the most important skills for data science and business analytics professionals. It helps them start from formulating business problems to communicating model results to stakeholders or business managers.

  • In this webinar, Madhukar will talk about the following challenges and provide people with frameworks and best practices on structured thinking:
  • How to take ambiguous business problems and then break them into structured data science problems?
  • How to present your analysis and business insights in an impactful manner?
  • How to do clear and structured communications which people can easily understand?

Key Takeaways:

  1. Introduction
  2. Data science framework to solve a business problem
  3. Two Shine case studies:
    1. ARS (Application Relevance score)
    2. JAM (Job alert mailers)

4. Going Beyond your First Machine Learning Project

So you’ve picked machine learning as your future career. You successfully completed your first machine learning project as well. Great! But what’s next? How do you go beyond the basics and take that next step, the big leap, that will make you industry-ready?

How can you build your profile in machine learning that will take you beyond the basics and into the realm of what the industry wants?

Akshay draws on his own experience, rich machine learning background, and his knowledge to help you navigate the often-tricky machine learning waters!

Key Takeaways for this webinar:

  1. A clearer understanding of building Machine Learning models.
  2. Importance of subtle nuances eg: data collection, cleansing, and exploration
  3. Getting one step closer to building production-level models and pipelines.
  4. Understanding of some basic image processing concepts for working on computer vision and machine learning problem

Data Science is a career path that requires problem-solving which can’t be fulfilled by just learning bookish Data Science concepts. You must master the concepts along with the projects to become an industry-ready professional. That’s why Analytics Vidhya’s AI and ML Blackbelt+ program offers 14+ courses along with 25+ real-life projects!

 

5. Data Science Careers – Which Role to Go for? – Mathangi Sri

Data Science might be the “Sexiest Job of the 21st century” but let’s not forget that there are a number of Data science-based roles to explore like a Business analyst, data engineer, and so on.

But which role is suitable for your skillset? While most folks gravitate towards a data scientist’s role – keep in mind that there are a whole host of roles in data science that are going vacant. Don’t go with the flow – you must understand the different roles available and map your skillset according to them.

In this webinar, Mathangi Sri helps you do just that! Mathangi brings 16+ years of experience in the data science space and she has seen a plethora of roles in her career. She talks about what these different data science roles are and what each role requires (among other things).

End Notes

In this article, we discussed the 5 top career-related data science webinars. If you are a data science aspirant these recordings will definitely help you ace in your career. Many industry experts help you navigate through the field of data science.

A mentor is very crucial to help you build and navigate through your career. The AI and ML Blackbelt+ course along with 14+ courses and 25+ projects offer 1:1 mentorship sessions so that you never get behind your goals. These expert mentors help you customize your learning path according to your career goals.

We recommend you go through these career-related articles to better navigate your journey in Data Science-

Let me know your thoughts on these expert sessions. You can check out the upcoming webinars here.

data science careerexpert talkswebinar
Ram Dewani
Ram Dewani

Product Growth Analyst at Analytics Vidhya. I'm always curious to deep dive into data, process it, polish it so as to create value. My interest lies in the field of marketing analytics.

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

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