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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

How to Get a Personalized Data Science Roadmap?

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Analytics Vidhya Last Updated : 26 Jul, 2023
6 min read

Introduction

In today’s data-driven world, many choose to make a career in data science but don’t know how to proceed. Want to know which career path provides sure shot career success? Personalized data science roadmap is the answer!

A personalized career roadmap is crucial for data scientists due to the multifaceted nature of the field and the diverse career paths available. Data science encompasses various skills, from programming and statistical analysis to machine learning and domain-specific expertise. Each data scientist possesses unique strengths, interests, and career aspirations, making a one-size-fits-all approach inadequate. In this article we will discuss the importance of personalized data science roadmap!

Table of contents

Need for a Data Science Roadmap

Data science is an interdisciplinary and vast field. It encompasses a variety of fields like machine learning, data engineering, statistics, data analysis and others. Learning each of these is not only time-consuming but also an inefficient method to progress. A roadmap for data science guides the candidates through their careers smoothly by imparting the following benefits:

  • Clarity in direction: Aids in deciding the right skill and knowledge to learn at a specific stage of your learning and working phase 
  • Efficient learning: It guides you to begin your journey with specific topics, thus avoiding the hit-and-trial method. 
  • Goal setting: The data scientist roadmap helps set achievable goals significant to your field. 
  • Specialization: It eases the choice of career paths by providing enough information to choose about each profession, such as business analyst, data analyst or engineer and machine learning engineer. 

What is a Personalized Data Science Roadmap? 

A data science roadmap is a tailored plan or guide for individuals to progress in their careers in data science. Concerning the vastness of data science, a roadmap for data science differs for individuals depending on their career choices, skills, interests, background and inspirations. It helps choose the most suitable pathways to further achieving one’s career goals, leading to a quicker and undistracted journey. Getting hold of a roadmap should be the preliminary step while choosing the appropriate course or learning skill in data science due to the following reasons:

  • The data science roadmap focuses on unique strengths and weaknesses to build their skills and expertise. 
  •  It sets clear and realistic goals along with a structured approach. It breaks down long-term goals into short-term ones for easy achieving and providing a sense of accomplishment.
  •  It aids in adapting to a specific career aspired in data science. It involves learning every component of the specifically chosen field. 
  •  It saves time and resources by filtering the overwhelming amount of information available on the internet. Focusing on practical application of the skill further enhances the chances of gaining the job. 

Successful Data Science Roadmap Unleashed!

The following steps will help you follow a fool-proof process towards your dream career in data science:

Personalized Data Science Roadmap

Develop ML Skills 

Important Machine learning skills are crucial in data science. They involve working on exploratory data analysis and data cleaning for summarization and error removal from the datasets. Learning feature selection and engineering for data extraction help enhance the performance of ML algorithms. Some of the important common models are linear and logistic regression, decision trees, K-nearest neighbor, Naive Bayes, K-means, Gradient boosting machines, XGBOOST and Support Vector Machines (SVM). Their purpose, functionality and applications differ in data science projects. 

Also Read: Top 20 Machine Learning Projects

Develop Problem-Solving Skills 

Data science jobs require scientists to analyze complex datasets. Numerous problems arise, requiring innovative and cost-effective solutions. Further, data exploration, model selection, hyperparameter tuning and model performance optimization are some of the tasks requiring problem-solving skills. These skills allow for handling challenges with creativity and refined approaches. 

Work on Hackathons and Projects 

The hackathons and projects provide hands-on experience in real-world scenarios in chosen fields. Updating the learners on current trends and required skills provide the opportunity to build a portfolio expressing your specific domain expertise. It also showcases one’s adaptability and independent will to learn and apply the skills. 

Develop Storytelling Skills

These skills are important for communication in data scientist jobs that require interaction with stakeholders, teams, and multiple technical and non-technical audiences. The ability to find insights and present them in a simple manner understandable to everyone involved is one of the key skills that need to be developed. It is also significant in driving critical business decisions and connecting the information with specific business objectives while working on data-driven presentations. Read our article to know more about data storytelling.

Build AI ML Portfolio 

The portfolio showcases diverse projects handled by the candidate and the ability to provide end-to-end solutions. It must be documented with data sources, code, methodology, problem statement and quantified form of the results in detail. Develop a GitHub repository, add readme files and exhibit participation in different projects. 

Start Deep Learning Journey 

Deep learning comes under the broader aspect of machine learning methods. It is based on ANN and involves gaining familiarity with supervised, semi-supervised, and unsupervised learning. Candidates get to learn about Convolutional Neural Networks or CNNS, Long Short Term Memory (LSTM) and autoencoders as a part of deep learning. Additionally, gaining working experience in deep learning frameworks, such as PyTorch and TensorFlow, boost knowledge and expertise. 

Learn NLP 

It is the current trending field owing to the high rise in the development of AI chatbots. Natural Language Processing is required for other tasks like abundant text-based data analysis, customer sentiment analysis, information retrieval and similar jobs. You must cultivate important skills in word embeddings, tokenization, topic modeling algorithms, language models, text classification and information retrieval. 

Also Read: Top 7 NLP Books Every Data Scientist Must Read

Learn Computer Vision 

It includes teaching machines about interpretation and understanding information from images and videos. It involves concepts of pixel operations, basic image processing techniques, image representation, and color spaces. Besides, techniques like image filtering, edge detection, HOG or histogram of Oriented Gradients are required apart from familiarity with generative models like Generative Adversarial Networks and approaches like Faster R-CNN and YOLO. 

Computer vision | Personalized Data Science Roadmap
Source: Softweb Solutions

Learn Advanced Python and Software Development 

Python and software development is a highly important aspect in the field of data science due to dealing with large datasets, complex models and solution deployment. Familiarity with coding and Python libraries such as Pandas, sci-kit, TensorFlow and NumPy are recommended. Software development skills are required for enhancing code quality, scaling and performance optimization. These are also required for testing, debugging and data visualization using advanced Python libraries and thus require learning the associated skills. 

Deploy ML/DL Models 

Machine Learning (ML) and Deep Learning (DL) models predict, recommend and solve real-world problems. The learning algorithms, model interpretability and time series analysis in ML, Recurrent and Convolutional Neural networks are important for familiarity and understanding of DL models. Transformer for language processing and LSTM for sequence modeling are a few of the other important skills. 

Master Apache Spark

It serves as a valuable skill for big data and distributed computing. Apache Spark is the generally used open-source framework. To familiarise yourself with the tool, begin with learning the basics of Spark Core by understanding the Resilient Distributed Datasets and data partitioning. Gain understandability of higher-level abstractions, data frames, datasets, data loading and transformation, and spark SQL. Additionally, explore Spark’s MLib library for machine learning and graph processing with Spark’s GraphX while familiarizing yourself with other functionalities. 

Apache Spark
Source: InetSoft

Why Opt for Black Belt Plus Certification? 

The mentioned skills see the important participation of Machine Learning and Artificial Intelligence in Data science. The rising importance of the same made us design a well-curated course, Black Belt Plus Certification, covering each important aspect required for a data science job. Our mentors help you with a personalized data science career roadmap that aligns with your goals and aspirations.

Besides deeply covering the curriculum (including more than 22 tools, computer vision, NLP, Deep Learning and others), the program provides an opportunity to choose and gain experience from 50+ real-world projects. The 100% placement assistance serves the overall objective of each involved individual’s hard work, further guiding in combination with more than 105 personalized mentorship sessions. 

Conclusion 

Dedicating to becoming a data scientist while working randomly on different skills and tools without a specific goal is challenging and disheartening. The unsustainable method can be made efficient by simply curating a personalized data science roadmap with a focus on one’s abilities. It helps to gain expertise and gives a specific mission where every progress contributes to the ultimate aim. 

Frequently Asked Questions

Q1. What is the roadmap for data science?

A. The roadmap for data science refers to finding and choosing a specific field in data science depending on your goals, career aspirations and knowledge. 

Q2. How do I start a data science roadmap?

A. Begin with self-assessment and then accordingly set clear goals and choose the appropriate learning path suitable to your current and future goals and lifestyle. Focus on learning fundamentals and gaining expertise. 

Q3. Can I master data science in 6 months?

A. Data science is a broad field encompassing multiple topics. Mastering the same in 6 months is possible through dedicated and smart learning with prior information on basics. 

Q4. Is data science a future-proof career?

A. Data science is an exponentially growing field with increasing demand for human-based soft skills. AI and ML applications, data-driven decision making and cross-industry applications are some of the important fields requiring data scientists promising future-proof careers. 

careerdata sciencedata science roadmapmachine learningroadmapskills
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