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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

Top 10 Platforms to Practice Data Science Skills

Yana Khare
Yana Khare Last Updated : 12 Jul, 2024
7 min read

Introduction

Data science is one of the professions in high demand nowadays due to the growing focus on analyzing big data. Hypothesis and conclusion-making from data broadly involve technical and non-technical skills in the interdisciplinary field of data science. To be relevant and competitive in this rapidly evolving area, at least specific fundamental data science skills are essential, irrespective of the amount of experience one has to undertake. This article describes the features and price structures of some of the best platforms for practicing critical data science skills.

Overview:

  • Discover the essential data science skills, including programming, statistical analysis, machine learning, and data visualization, and how various platforms can help you master these areas.
  • Explore top platforms like Kaggle, Coursera, edX, and Udacity, and understand their unique offerings, including courses, projects, and community resources.
  • Understand the importance of practicing key data science skills through real-world applications, competitions, and hands-on projects to enhance your proficiency and build a strong portfolio.
Top 10 Platforms to Practice Data Science Skills

Table of contents

Key Data Science Skills

Data science is recognized as a group discipline, implying technical and non-technical competencies are needed. Here are some of the critical skills essential for data scientists:

1. Programming Skills

  • Python and R: They are general-purpose programming languages most used in data science because of their rich libraries and the general ease of executing statistical analysis and machine learning tasks.
  • SQL: Essential for database management and manipulation.

2. Statistical Analysis

  • Descriptive and Inferential Statistics: The other important topics that should be familiar to BI professionals with a statistical background involve data distributions, hypothesis testing, and confidence intervals.
  • Probability: Used in modeling uncertainty and also in making and drawing predictions.

3. Machine Learning

4. Data Visualization

  • Tools: Knowledge of Matplotlib, Seaborn, Tableau, and Power BI for designing appealing and meaningful figures and tables.
  • Storytelling with Data: The balance within the presentation of insights to make them visually compelling.

5. Data Wrangling

  • Data Cleaning: Verify data and check for errors and discrepancies.
  • Data Transformation: Converting data into a suitable format for analysis.

6. Big Data Technologies

  • Hadoop and Spark: Tools for handling and processing large datasets.
  • NoSQL Databases: Understanding databases like MongoDB and Cassandra.

7. Domain Knowledge

  • Understanding the specific industry context is necessary to make relevant and actionable insights.

8. Soft Skills

  • Communication: Effectively explaining findings to non-technical stakeholders.
  • Problem-Solving: Identifying problems and creating data-driven solutions.
  • Collaboration: Working efficiently in a team environment.

Also Read: How to Become a Data Scientist in 12 Months?

Importance of Practicing Key Data Science Skills

  1. Stay Current with Technological Advances: Data science is an actively growing discipline. Senior practitioners can more effectively do this by staying current with recent methods, instruments, and best practices.
  2. Enhanced Problem-Solving Abilities: When data science methodologies are employed in actual scenarios, it is considerably easier to grasp a complex situation and generate a superior result.
  3. Build a Strong Portfolio: Combining applicable projects with the kind of work one does allows one to build the right portfolio that the potential employer wants to see.
  4. Improve Technical Proficiency: Practice improves accuracy and many technical actions; writing the correct code, analyzing the right data, and creating accurate diagrams and dashboards become easier.
  5. Adapt to Various Data Types and Problems: Data scientists deal with different datasets and problems. Handling variance makes a researcher more flexible, especially when dealing with other data sets and states.
  6. Foster Innovation and Creativity: Applying real-life issues enhances problem-solving creativity and innovative ideas.
  7. Increase Employability and Career Advancement: Improving the skills required to advance a career is the only way. Applications that exhibit a commitment to learning and practical skills are a plus for employers.
  8. Effective Communication of Insights: Experience improves the ability to present scientific analysis results to clients in the simplest form that any stakeholder would need to make decisions.

Top 10 Platforms to Practice Key Data Science Skills

Kaggle

Popular data science portal Kaggle provides resources such as datasets, contests, and kernels (now called notebooks). Users may practice by participating in competitions, looking through public notebooks, and interacting with the community.

What can you learn?

Pricing Model

  • Free: Access to datasets, notebooks, and community discussions.
  • Competitions may have cash prizes funded by sponsors.

Coursera

Coursera offers courses in various data science disciplines through partnerships with prestigious institutions and businesses. With professional credentials and specializations, learners may pursue further studies in artificial intelligence, data analysis, and machine learning.

What can you learn?

  • Machine learning and artificial intelligence
  • Data analysis and visualization
  • Big data technologies
  • Statistics and probability
  • Programming languages like Python and R

Pricing Model

  • Free: Access to some course materials.
  • Paid: Full access to courses, graded assignments, and certifications. Subscription models or one-time payments per course/specialization.

edX

edX offers a wide range of classes from reputable universities. Its data science courses encompass various subjects and frequently involve hands-on learning activities that support acquiring theoretical knowledge.

What can you learn?

  • Data science fundamentals
  • Machine learning and AI
  • Data analysis and visualization
  • Big data and cloud computing
  • Domain-specific applications of data science

Pricing Model

  • Free: Audit courses with limited access.
  • Paid: Verified certificates and full access to course materials. Subscription models or one-time payments per course/program.

Udacity

Udacity offers data science Nanodegree programs, created in association with prominent figures in the field. Because these programs are project-based, students may develop portfolios highlighting their abilities. This platform for practicing data science skills also provides career services and mentoring.

What can you learn?

  • Data analysis and visualization
  • Machine learning and AI
  • Data Engineering
  • Programming in Python and SQL
  • Real-world projects and case studies

Pricing Model

  • Paid: Monthly subscription for Nanodegree programs. Discounts and financial aid may be available.

Udemy

Udemy offers a wide selection of courses on every facet of data science. The platform allows users to practice data science skills, and its adaptable learning framework allows users to advance quickly. Courses often feature practical tasks and projects.

What can you learn?

  • Data science basics to advanced topics
  • Machine learning and AI
  • Data analysis and visualization
  • Programming in Python, R, and SQL
  • Specific tools and technologies (e.g., TensorFlow, Pandas, Scikit-learn)

Pricing Model

  • Paid: One-time payment per course. Frequent discounts and sales are available.

Pluralsight

Pluralsight provides learning pathways and courses that build thorough data science competencies. The platform’s skill evaluations allow learners to track their progress and discover knowledge gaps.

What can you learn?

  • Data analysis and visualization
  • Machine learning and AI
  • Big data technologies
  • Programming in Python, R, and SQL
  • Professional development and soft skills

Pricing Model

  • Paid: Monthly or annual subscription. Free trial available.

LinkedIn Learning

Experts in the field teach various data science courses on LinkedIn Learning. Because the platform is connected to LinkedIn, students may showcase their finished courses on their accounts, increasing their workplace exposure.

What can you learn?

  • Data science and analytics
  • Machine learning and AI
  • Data visualization
  • Programming languages like Python and R
  • Business intelligence tools

Pricing Model

  • Paid: Monthly or annual subscription. Free trial available.

IBM Data Science Community

IBM provides webinars, tutorials, and various paid and free tools for studying data science. Practitioners may collaborate in a supportive atmosphere to exchange information and ideas within their data science community.

What can you learn?

  • Data science fundamentals
  • Machine learning and AI
  • Data analysis and visualization
  • Cloud computing and big data
  • IBM tools and technologies

Pricing Model

  • Free and Paid: Some resources and courses are free, while others require payment. Certification programs typically have fees.

Intellipaat

Data science courses from Intellipaat are available for free and for a fee, and they cover a lot of ground. The platform is useful for developing practical skills since it strongly emphasizes experiential learning through real-world projects.

What can you learn?

  • Data science fundamentals
  • Machine learning and AI
  • Big Data and Hadoop
  • Data visualization
  • Cloud computing

Pricing Model

  • Free and Paid: Free resources, paid courses, and certification programs are available. Discounts and financial aid may also be available.

GUVI

GUVI offers courses in several languages, with an emphasis on teaching data science and coding. The platform’s community support and dynamic learning environment enable learners to improve their abilities successfully.

What can you learn?

  • Programming languages like Python, Java, and C++
  • Data science and analytics
  • Machine learning and AI
  • Web development
  • Real-world projects and case studies

Pricing Model

  • Free and Paid: Some courses are free, while others require payment. Discounts and financial aid may be available.

Conclusion

Practicing and improving critical skills as data science develops and grows is crucial. By utilizing these platforms to practice data science skills, aspiring data scientists may build a broad skill set that equips them for various possibilities and difficulties in the field. Success in this fast-paced sector requires constant practice and learning, regardless of your goals for the trip or career advancement.

If you want to become a data scientist and looking for a program to kickstart your journey, then checkout our AI/ML BlackBelt Plus Program.

Frequently Asked Questions

Q1. How can I practice data science skills?

A. Practice data science skills by working on real-world projects, participating in online competitions like Kaggle, and engaging in open-source projects. Utilize datasets from platforms like UCI Machine Learning Repository, and practice coding and statistical analysis with tools such as Python, R, and SQL.

Q2. How do I Upskill myself in data science?

A. Upskill in data science by enrolling in online courses, attending workshops, and earning certifications. Regularly read research papers, follow industry blogs, and participate in data science communities. Focus on learning advanced topics like machine learning, deep learning, and big data technologies to stay current.

Q3. Which platform is best for learning data science?

A. Coursera is highly regarded for learning data science, offering courses from top universities and institutions. Platforms like edX, Udacity, and LinkedIn Learning also provide comprehensive data science curricula, hands-on projects, and interactive learning experiences catering to beginners and advanced learners.

Q4. What is the salary of a data scientist in India?

A. As of 2024, the average salary of a data scientist in India is around ₹10-15 lakhs per annum. Entry-level positions start at approximately ₹5-7 lakhs, while experienced professionals earn upwards of ₹20 lakhs, with top-tier companies offering even higher packages.

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Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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