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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

Importance of Internships in Data Science

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Analytics Vidhya Last Updated : 08 Apr, 2022
5 min read

Introduction

Internships and apprenticeships are two of the most popular methods of learning on the job and gaining crucial skills. The concept of an apprentice started dying out once masters of various sciences and crafts were replaced by employers. Eventually, internships took over from the 20th century as a standardised approach for gaining domain experience.

Internships are officially commissioned by the company and also feature documentation or certificates that justify an intern’s time at the company. Internships are essential for gaining hands-on experience and are one of the best methods of adapting to job roles that you like. For instance, if you complete an internship as a Data Scientist or a data analyst, then you will become prepared to function in any of the respective professions with ease. Internships also make other companies more likely to hire you, assuming that you are already trained in the job role.

Another factor we must take into consideration is the tools and technologies that are associated with sectors such as Data Science. There are many software and methodologies which are domain-specific and are not necessarily covered in degree programmes. Thus, individuals can use the time during their internship to acquire these necessary skills. Yes, there are online courses that teach these skills but an internship allows companies to believe that you do not need any further technical training (unless a company is using different technologies). There are programmes such as Data Science Immersive Bootcamp that can provide you with real-world job training or internships as well as teach you all the necessary skills. 

There are also various soft skills that internships help you pick up. Internships are the best approach for gaining experience for freshers. Even if you are an exceptional student, being an intern first is recommended as it is sometimes a requirement for top MNCs that are dealing with domains such as Data Science.

Benefits of Data Science Internships

Joining and then completing an internship in Data Science can help you in a lot of ways along your path to becoming a Data Scientist, a data analyst or a data engineer. Internships serve as proof of your accomplishments and your foundational abilities. Through your internship and the projects you have worked on, employers can find out your capabilities and how well you fit inside a Data Science process or a pipeline. Also, without an internship, it is almost impossible to get jobs as a Data Scientist.

 

Let us check out some of the main reasons why internships are essential for Data Science jobs.

 

Crucial Skills

Data Scientist skills

The best thing about internships is that interns are not expected to know much when they join and are able to learn while being on the job. Unlike degree programmes and courses, internship roles require interns to carry out many tasks that help them gain practical experience. This helps one acquire enough knowledge about all the necessary tools, technologies, techniques and methodologies.

Formal education mostly covers foundational topics and not specialised tools and skills. Thus, one will always learn something new while being an intern. Generic curriculum from formal education generally features outdated technology while companies operating on the ground adapt to modern practices for meeting business requirements.

For example, you might have been taught Python for programming and Excel for foundational analytics during your education. However, the company you are working for requires you to use Microsoft Power BI, Azure and various libraries for Python such as Matplotlib. Nowadays, Power BI caters to various operational and strategic requirements of a company. By learning other technologies from your internship, you will seem more alluring to employers who are also using the same systems or tools.

Similarly, you can learn skills related to data pre-processing, data mining, data warehousing and tools associated with cloud computing, artificial intelligence and machine learning. 

Experience and Domain Knowledge

Internships in Data Science

One can gain crucial experience with the help of internships. From the daily tasks during internships, interns can learn important domain information that will help them become better employees in the future. With enough exposure during your internship, you can even become a domain expert. For example, you might become excellent in noise removal or visualisation just by carrying out these job responsibilities during your internship.

With more experience and knowledge, you will also feel more confident, essentially reflecting your skills through high-quality work. Also, you will be adding value to the company you are working for, providing you with enough job satisfaction. Many companies have specialised training facilities and internal resources that interns can use for growing.

Employability

jobathon

Freshers find it hard to get good jobs due to not having any experience. A lot of companies assume that it will take additional time and money to train freshers and thus, many of them prefer freshers who have completed an internship. Companies expect interns to already have domain knowledge and an understanding of how Data Science processes function. 

This enables people who have completed their internships to become more employable. Let us take an example where there are two candidates where one has completed an internship and the other has not. In this kind of situation, it is almost guaranteed that a company will choose a candidate who has completed an internship. Many internships also convert into full-time jobs upon the completion of the intern period.

Networking and Career Prospects

Career in Data Science

By joining an internship, you can grow your network and become acquainted with more people working in the field who wish to be involved in the future. This can help you learn the opinions of other professionals in the domain. You can also identify the future of the sector better with the help of other senior resources. Other professionals working in Data Science can help better guide you in terms of upskilling yourself and your career. For instance, domain experts might recommend a tool such as SAS to upskill yourself in.

For Data Science, growing your network is crucial to stay updated about the latest market trends and innovations in technology. There are also many great career prospects you might find out about after joining an internship. Many companies also refer their interns to their partners or to other organisations.

Conclusion

Internships help you gain valuable guidance that you will not be able to find during your formal education. College professors and programme instructors also provide important advice and suggestions in terms of your career, but internships allow you to take the wheel into your own hands and experience the domain of Data Science on your own.

An internship is like a journey that allows you to gain skills without any compulsory cost. As a matter of fact, many internships even compensate interns, allowing them to learn while they earn. Finally, without an internship, it might be extremely hard to get a good data scientist job, if not impossible. Bootcamps such as Data Science Immersive Bootcamp by Analytics Vidhya can solve this problem by offering a 100% job guarantee. Read more about a comprehensive comparison among Data Science bootcamps vs degree vs online courses here. 

 

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Analytics Vidhya

Analytics Vidhya Content team

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