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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

Ways to Transition into Data Science: Fresh Graduates and Experienced Professionals up to 5 years

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Analytics Vidhya Last Updated : 12 Nov, 2024
6 min read

data science transition

Introduction

Data Science is becoming one of the most lucrative sectors that professionals want to switch to from other domains. It is the same for freshers as well, with a growing number of fresh graduates wishing to join various professions that come under Data Science. However, there is a major problem that both candidates and employers are facing. 

The skills gap in this domain is a growing problem. Many candidates are not accustomed to Data Science tools that are currently being used, thus requiring employers to additionally train them. Similarly, there is a huge lack of skilled personnel in Data Science and this is extremely troublesome for employers. 

Even if top MNCs decide to recruit candidates with fewer skills in Data Science, these companies will expect higher academic qualifications. This is another problem that professionals from other fields, such as software developers face when trying to join Data Science. However, these problems are easy to fix. 

More than high academic qualifications, companies now are looking for individuals who are job-ready. Why? It is easy to integrate these kinds of individuals into existing Data Science pipelines or processes. Companies do not need to spend extra resources to get skilled individuals deployment-ready and get a better ROI (return on investment) on the recruits during their initial months. 

Let us check some of the methods we have available to us for transitioning into Data Science.

Steps for Becoming a Data Science Professional

One of the most essential requirements of working in this sector is knowing how to use various software, libraries, programming languages and databases. After that, you must strengthen your foundations in mathematical algorithms and statistics. Also, there are various soft skills that one must learn to become a part of this domain. 

Here are some essential things you must consider if your aim is to join the field of Data Science.

Programming Languages and Libraries

Transition into Data Science

Learning a programming language is essential for Data Science. You can choose a programming language such as Python, R or Scala. Python is comparatively easy to learn and is one of the most preferred languages for Data Science. However, for statistically-inclined tasks, R is always the better option. R also helps you create amazing visualisations and helps you plot graphs. R is a programming language built for statisticians and meant for statistical analysis.

Now, for the programming language you choose, you must also learn how to use their respective Integrated Development Environments (IDEs) such as Anaconda for Python or RStudio for R. Many Data Scientists also use VBA or Visual Basic for Applications. Now, when it comes to libraries for Data Science, you should get accustomed to Pandas, Scikit-Learn, Numpy, SciPy and Matplotlib for Python. These libraries can help you carry out pre-processing, mathematical, analytical and plotting tasks which are essential for Data Science.

Databases, Distributed File Systems and Cloud Computing

Cloud Computing

One of the primary concerns in Data Science is storing and managing data. This is why learning how to use DBMS (Database Management Systems) is essential. In order to utilise data from these databases or for CRUD (Create Read Update Delete) functions, you must also learn Standard Query Language or SQL. Some solid relational databases that you can learn to use are MySQL, MariaDB or Microsoft SQL Server.

Cloud computing skills are also heavily appreciated by employers. Thus, gaining skills for platforms such as Amazon Web Services (AWS), Microsoft Azure or Google Cloud Platform (GCP) can turn out to be helpful. Learning cloud computing also helps one in becoming a data architect or a data engineer.

Mathematics and Statistics

Mathematical concepts and statistical theories are absolutely essential for Data Science. Statistical inference, data summarization, statistical distribution and other statistical functions are also crucial for many data-driven tasks. Statistical techniques can help analyse data and make predictions, further helping companies make better business decisions. Data-driven actions are always backed by statistical models. 

Similarly, many algorithms and mathematical concepts are used when working with Data Science projects, especially those powered by Machine Learning and other implementations of Artificial Intelligence. You will also need to learn techniques such as classification techniques, regression techniques and Bayesian Inference.

Data Science Skills, Tools and Technologies

There are a few skills in Data Science that are absolutely essential. These skills are:

  • Data processing
  • Data cleaning
  • Data warehousing
  • Data transforming
  • Data mining
  • Data analysis
  • Data modelling
  • Data visualisation

Now, in order to effectively carry out the above tasks and other common Data Science job roles, there are multiple tools and technologies that you must learn. Data pre-processing and data cleaning can be handled by libraries using languages such as Python. Now, for data warehousing, one can use technologies such as Amazon Redshift. 

Meanwhile, for data analysis, tools such as Tableau and Excel are very popular. For more business-centric analytics, you can also learn tools such as Microsoft Power BI. If you are looking for more statistically-inclined tools, you can also learn how to use SAS. SAS is the top analysis technology for statistical analysis.

These skills are absolutely crucial for becoming a Data Scientist. However, if you wish to become a data engineer or a data architect, you must place more focus on learning how to integrate data, build data models and make data more accessible for data-driven systems or for organisational use. Programs such as the Data Science Immersive Bootcamp by Analytics Vidhya can help you learn these key skills.

Projects and Real-world Applications

Transition into Data Science

One of the best ways to become a data scientist and take the first step to make a transition into data science is to have deployed projects. It is even better if these projects can be used for real-world applications. Having solid projects also makes employers more likely to hire you. More than anything, projects allow you to gain hands-on experience. Try using real-world problems for thinking of innovative solutions and working on them. 

 

How to Transition into a Data Science Professional? 

Here are the best ways to transition into a Data Science professional:

  • Join a Bootcamp: Bootcamps are one of the best methods for switching to a Data Science profession. Bootcamps are great for working professionals as well as freshers. These programmes are designed to teach you all the necessary skills that you will need as a Data Scientist or any other Data Science professional.
  • Find an internship: If you are a fresher without any Data Science skills and experience, it is a great idea for you to join as an intern. Becoming an intern ensures you gain some work experience and you will also get the opportunity to learn all the skills you will be using for your daily tasks.
  • Upskill yourself or gain certifications: In order to transition into a Data Science job, you must upskill yourself by learning new technologies and skills. Getting certified in these skills and tools also make you more desirable to companies.
  • Join a Degree Programme: Degree programmes might not be the right fit for everyone as they take a lot of time and working professionals find it very hard to give time to a solid degree programme. However, if you are willing to go for a master’s, you can go for one that is focused on Data Science. 
  • Search for recruitment drives and stay updated: You must also take care to stay updated about vacancies and requirements in various companies. There are many recruitment drives held by top MNCs throughout the year and you can easily apply as a candidate.

Conclusion

Data Science is essential for Finance, IT, Marketing, Healthcare, Manufacturing and most other sectors. If you have the necessary skills and if you are dedicated, you will not find it hard to join a career in Data Science or make a transition into the Data Science industry. You can even use the experience you have gained from your previous profession in specific fields of Data Science such as analytics.

For example, if you are a finance or management professional, you can go for business analytics or financial analytics. Similarly, if you are from marketing, you can use your experience for marketing analysis and customer sentiment analysis. Professionals from other sectors such as Statistics or Software Development will also find it easy to get into statistical processes and data engineering respectively. 

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