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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

10 Questions Every Data Science Beginner Asks (with Answers and Resources)

Ram Dewani
Ram Dewani Last Updated : 21 Jun, 2022
11 min read

Overview

  • Data science beginners tend to ask some common questions about their career and learning path
  • Here are 10 such questions with comprehensive answers to help all data, science beginners

 

Introduction

Starting a data science career is appealing but it’s an obstacle-filled journey. You’ll notice how a few key questions constantly keep popping up – Where to start? What to learn and how to learn? How to find the right resources for data science?

If you’ve ever asked these questions or are struggling to find the answers – you’re not alone!

Data Science is a relatively new field and is still in its nascent stage (yes, even in 2020). It becomes hard to decode each and every puzzle it offers. And a major challenge for data science beginners is that the knowledge about data science is scattered, and every different resource follows a different approach. So amidst all this confusion – how can you become a successful data scientist?

data science beginner questions

In this article, I will discuss the 10 most asked questions by data science enthusiasts and beginners. These will help you figure out different aspects of your data science career, including your resume, interview process, and other best practices.

Additionally, here is a data science roadmap defining the milestones in your data science journey. Use this roadmap to track your Data Science Journey, see where you stand and what should be your next step. Click here to download the data science roadmap.

Now, this article is for those folks who are trying to figure out their way in the data science industry. The people enrolled in the Analytics Vidhya’s don’t undergo such problems because they are connected with their mentors all the time. You can also go from zero-to-hero by undergoing the Certified AI & ML BlackBelt Plus Program!

 

1. What are the Most Common Mistakes Data Science enthusiasts make in an Interview?

Let’s discuss the most common mistakes made by data science enthusiasts one-by-one:

  • Preparing only theoretical topics without applying them

    Let’s say that you are in the middle of a data science interview and the interviewer asks you – What is random forest and how does it work? Being a simple and standard question you answer the question smoothly. Then the follow-up zinger comes – How would you improve the performance of the model in the context of the business?
    Now, unless you have solved a data science problem previously using random forest and tuned its hyperparameters, you won’t be able to give a proper answer which can lead to doubt in the mind of the interviewer.

  • Assuming what you see in ML Competitions is what Real-Life Jobs are like

    There’s no better to prepare for a data science role than participating in machine learning competitions. This is undeniable. The problem is it doesn’t make you an industry-ready professional. Usually, the interviews include case studies that test your problem-solving skills and domain knowledge and these are usually gained with experience.

  • Using too many Data Science Terms

    Your resume is a profile of what you have accomplished and how you did it – not a list of things to simply jot down. When a recruiter looks at your resume, he/she wants to understand your background and what all you have accomplished in a neat and summarized manner. If half the page is filled with vague data science terms like linear regression, XGBoost, LightGBM, without any explanation, your resume might not clear the screening round.

  • Not working on Communication Skills 

    Communication skills are one of the most underrated and least talked about aspects a data scientist absolutely MUST possess. You can learn all the latest techniques, master multiple tools, and make the best graphs, but if you cannot explain your analysis to your client, you will fail as a data scientist. This is what the interviewer will be testing in the interview process.

 

2. I’m coming from a non-technical background so why should I learn about software engineering?

data science beginner questions - software_engineering_machine_learningHonestly, this is one of the most asked questions and I hope your doubts will be cleared after reading this.

The end goal of every data science project is to deploy the project in production. So, no matter how accurate your model is, it is still incomplete without the last step as we will be discussing it further in the article.

To write a high and good quality code that won’t cause havoc during the production stage, it is necessary to know the basics of some of the software engineering subjects like – basic lifecycle of software development projects, data types, compilers, time-space complexity, etc.

Writing efficient and clean code will help you in the long run and help you collaborate with your team members. Again, you don’t need to be a software engineer but being clear with the basics will help you. 🙂

 

3. Do I need to be good at programming to succeed as a data scientist?

I will reiterate here – You don’t need to be “great” at programming but you must be “Good Enough” at programming. Let me ask you a question – What is your preferred choice of language for data science? Python, R, SAS, or perhaps Julia? Let’s take an example of Python here.data science beginner questions - programming

 

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. You should be able to smoothly write custom functions, generators, and so on. Even if you don’t know how to optimize your code at this stage that is fine. You should be able to transform your well-thought operations into the form of code.

You don’t need to master all the language 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 Certified AI & ML BlackBelt Plus Program where machine learning experts teach you Excel, SQL, Python, and its libraries from simple Pandas to advanced Keras!

Want to start programming for your data science career? Here are a few resources –

  1. Python for Data Science
  2. Pandas for Data Science
  3. Complete Python Tutorial to learn Data Science

 

4. What is model deployment and why should I learn about it?

deploymentOnce you have made the complete data science project, it is time for the intended user/ stakeholder to reap the benefits of the predictive power of your machine learning model. In simple words, this is model deployment. This is one of the most important steps from a business point of view but also the least taught one.

Let us take an example here. An insurance company has initiated a data science project which uses Vehicle images from accidents to assess the extent of the damage. The data science team works day and night to develop a model that has a near-perfect F1 score. After months of hard work, they have the model ready and the stakeholders love its performance but what after that?

Remember that the end-user, in this case, are the insurance agents and this model needs to be used by multiple people at the same time who are NOT data scientists. Therefore they’ll not be running a Jupyter or Colab notebook on GPUs. This is where you need a complete process of model deployment.

This task is usually done by machine learning engineers but it varies according to the organization you are working in. Even if it is not the job requirement of your company, it is very important to know the basics of model deployment and why it is necessary.

 

5. What are the career opportunities in Data Science?

Data Science would not be known as the “Sexiest Job of the 21st century” if it didn’t provide luring opportunities. It is a $38 billion market and it is expected to reach $140 billion by 2025. It is really exciting to be a data scientist in this decade.There are ample job opportunities in the world of data-based roles. You can become a business analyst, data analyst or even the advanced role of machine learning engineer or deep learning engineer are available. If you prefer to dive into data science, then let’s look at how the typical career path maps out.

A Data Scientist’s strengths lie in coding, mathematics, and research abilities and require continuous learning. Once you have become a data scientist you can expect to follow this general path and grow into this field which will lead you to become a data science leader. Once you have the industry knowledge and experience you can expect to delve into product roles or even end up becoming an entrepreneur. Exciting, isn’t it? You can refer to the below resources to pave your journey for a data science role –

6. What skills should be listed down on Resume for Data Science Jobs?

There is an endless number of skills that you can mention in your resume but the question is – should you cramp 10 of your unproven skills or 3-4 strong hands-on skills? The answer is, as you might have guessed, the latter. The interviewer will be expecting you to be good with each skill you have mentioned.Let us take up a few points one-by-one and discuss them:

  • Prioritize skills according to the job role
    The interviewer has a very specific demand for hiring any personnel. You have got to mold your resume according to the job description. For example, the job description requires a candidate with a strong Python and machine learning stronghold. Whereas you have a stronghold over Python, machine learning, and deep learning. To show off, you include all the projects related to deep learning and miss out on machine learning projects and skills. This might set off the recruiter and you will lose your chance to a coveted job.
  • Mention data science projects
    As I mentioned in the above questions, practical knowledge counts more than theoretical knowledge and data science projects are a clear way to showcase your skills. Try to mention projects which will showcase each of the skill you have included.

  • Don’t forget your GitHub profile

    Nowadays, a GitHub profile is a must if you want to go for a data science job unless the required skills are only Excel or SQL. A Github profile instills confidence, trust, and flexibility to check out any project that you have mentioned in a resume.  It is a sure shot way to win the heart of the recruiter.

  • The overall resume counts

    No matter how capable you are unless the resume gives a clear picture of you or your skills, you won’t go to the next stage. Therefore, be precise in the format, font, structure of your resume. You can check out the below video posted by Google. It has some amazing guidelines and recommendations for building a great resume.

 

7. Do I need to know statistics in order to land a data science role?

It is said that:

“Statistics is the grammar of Data Science”

So to give a short answer to this question – Yes you need to know statistics in order to land a data science job. But don’t be afraid. You are not required to go through a master’s course in statistics. There a couple of topics/concepts that you must have commands on and you are good to go –

  • 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 so here I am mentioning some resources, else you can checkout Certified AI & ML BlackBelt Plus Program which covers all about statistics and data science comprehensively –

 

8. Should I participate in hackathons? Will that help me in getting a job?

Data Science competitions provide an amazing opportunity and platform to showcase the skillset that you have developed over a brief period. It helps you understand the domain, the techniques, the flow of a machine learning project and gives a good sense of direction.A majority of recruiters give keen attention to past hackathon performances. So if you haven’t started participating now is the time. Don’t worry if you have a fear of hackathon submission, it can be overwhelming sometimes. You can check out HackLive – a guided community hackathon through which you can master the art of participating in a hackathon.

Inspired to participate in hackathons? Here are some articles to get you started on your journey –

 

9. What are the benefits of a Data Science certification?

There are definitely some advantages that come along with a data science certification, it reflects your interest in the field of data science but there’s a caveat – due to the boom of data science, there has been a massive uptake of these courses which makes them common or general. So what can you do in this situation?If you are to take up free certification courses provided by multiple MOOC websites, it will definitely reflect your interest in this field but it won’t help you stand out. To stand apart from the crowd, you will need to take up a course that provides you with industry exposure and high-quality projects. A certification that is taken up as a standard to measure great talent.

Certified AI & ML BlackBelt Plus Program is one such course that will provide with you each and everything you will need to become a highly valuable professional in the data science industry. It isn’t just about certification, it is about the quality and guidance that comes with it.

To conclude, if you go for certification then decide wisely. Take up a certification that the industry values.

10. Bonus question – How do I become an industry-ready data science professional?

It is perhaps the most asked question by every data science professional. But first, what is an industry ready professional? This is someone who has hard skills as well as the soft skills to take on the job without specialized training from the organization. These professionals make an impact from day one.After talking to hundreds and thousands of data science professionals, Analytics Vidhya has come up with the Certified AI & ML BlackBelt Plus Program which includes everything you will need to become an industry-relevant professional.

One of the great advantages of the Certified AI & ML BlackBelt Plus Program is that you are not just given 14+ high-quality courses and 25+ real-life projects, you are provided with a mentor who’ll guide you from day one and also customize the goals according to your needs.

 

End Notes

In this article, we have discussed the 10 most important questions that may come to the minds of data science beginners and enthusiasts. Hope this article clears some of your doubts. You can clear your doubts altogether by undergoing the Certified AI & ML BlackBelt Plus Program.

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 with the questions that arose in your mind at the beginning of your data science journey. Also, if there are some other basic questions/doubts that should be shared with the community then feel free to leave it in the comment section.

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.

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Responses From Readers

Clear

Kurt Knuttson
Kurt Knuttson

Hey Ram, Thanks for sharing. Data and analytics are used every day to help businesses drive efficiencies, glean deeper operational insights, and ultimately generate more revenue. However, the impact of data science reaches far beyond the business sector and is helping to solve some of mankind's most pressing issues. Regards, Kurt Knuttson

123

Anirudh
Anirudh

Thanks a lot for sharing this. One of my friends is thinking to start his career in data science and I will share your article with him and hope it helps him to get an idea of data science

123

Vinayak
Vinayak

Hey Ram, Can you suggest any other hackathon community apart from HackLive which can help me elevate my data science game even more?

123

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