Free Platforms to Build Your Data Science Portfolio and Land Your Dream Role

Top Platforms for Building Data Science Portfolio

Build an irresistible portfolio that hooks recruiters with these free platforms.

Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.

1. GitHub

2. Kaggle

3. LinkedIn

4. Medium

5. MachineHack

6. DagsHub

7. HuggingFace

Resume key words for data scientist role explained in points:

1. Data Analysis:

- Proficient in extracting, cleaning, and analyzing data to derive insights.

- Skilled in using statistical methods and machine learning algorithms for data analysis.

- Experience with tools such as Python, R, or SQL for data manipulation and analysis.

2. Machine Learning:

- Strong understanding of machine learning techniques such as regression, classification, clustering, and neural networks.

- Experience in model development, evaluation, and deployment.

- Familiarity with libraries like TensorFlow, scikit-learn, or PyTorch for implementing machine learning models.

3. Data Visualization:

- Ability to present complex data in a clear and understandable manner through visualizations.

- Proficiency in tools like Matplotlib, Seaborn, or Tableau for creating insightful graphs and charts.

- Understanding of best practices in data visualization for effective communication of findings.

4. Big Data:

- Experience working with large datasets using technologies like Hadoop, Spark, or Apache Flink.

- Knowledge of distributed computing principles and tools for processing and analyzing big data.

- Ability to optimize algorithms and processes for scalability and performance.

5. Problem-Solving:

- Strong analytical and problem-solving skills to tackle complex data-related challenges.

- Ability to formulate hypotheses, design experiments, and iterate on solutions.

- Aptitude for identifying opportunities for leveraging data to drive business outcomes and decision-making.

Resume key words for a data analyst role

1. SQL (Structured Query Language):

- SQL is a programming language used for managing and querying relational databases.

- Data analysts often use SQL to extract, manipulate, and analyze data stored in databases, making it a fundamental skill for the role.

2. Python/R:

- Python and R are popular programming languages used for data analysis and statistical computing.

- Proficiency in Python or R allows data analysts to perform various tasks such as data cleaning, modeling, visualization, and machine learning.

3. Data Visualization:

- Data visualization involves presenting data in graphical or visual formats to communicate insights effectively.

- Data analysts use tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn to create visualizations that help stakeholders understand complex data patterns and trends.

4. Statistical Analysis:

- Statistical analysis involves applying statistical methods to analyze and interpret data.

- Data analysts use statistical techniques to uncover relationships, trends, and patterns in data, providing valuable insights for decision-making.

5. Data-driven Decision Making:

- Data-driven decision making is the process of making decisions based on data analysis and evidence rather than intuition or gut feelings.

- Data analysts play a crucial role in helping organizations make informed decisions by analyzing data and providing actionable insights that drive business strategies and operations.

Most Important Mathematical Equations in Data Science!

1️⃣ Gradient Descent: Optimization algorithm minimizing the cost function.

2️⃣ Normal Distribution: Distribution characterized by mean μ\muμ and variance σ2\sigma^2σ2.

3️⃣ Sigmoid Function: Activation function mapping real values to 0-1 range.

4️⃣ Linear Regression: Predictive model of linear input-output relationships.

5️⃣ Cosine Similarity: Metric for vector similarity based on angle cosine.

6️⃣ Naive Bayes: Classifier using Bayes’ Theorem and feature independence.

7️⃣ K-Means: Clustering minimizing distances to cluster centroids.

8️⃣ Log Loss: Performance measure for probability output models.

9️⃣ Mean Squared Error (MSE): Average of squared prediction errors.

🔟 MSE (Bias-Variance Decomposition): Explains MSE through bias and variance.

1️⃣1️⃣ MSE + L2 Regularization: Adds penalty to prevent overfitting.

1️⃣2️⃣ Entropy: Uncertainty measure used in decision trees.

1️⃣3️⃣ Softmax: Converts logits to probabilities for classification.

1️⃣4️⃣ Ordinary Least Squares (OLS): Estimates regression parameters by minimizing residuals.

1️⃣5️⃣ Correlation: Measures linear relationships between variables.

1️⃣6️⃣ Z-score: Standardizes value based on standard deviations from mean.

1️⃣7️⃣ Maximum Likelihood Estimation (MLE): Estimates parameters maximizing data likelihood.

1️⃣8️⃣ Eigenvectors and Eigenvalues: Characterize linear transformations in matrices.

1️⃣9️⃣ R-squared (R²): Proportion of variance explained by regression.

2️⃣0️⃣ F1 Score: Harmonic mean of precision and recall.

2️⃣1️⃣ Expected Value: Weighted average of all possible values.