Roadmap to becoming a data analyst

Key Skills You Need to Develop to Become a Successful Data Analyst

Soft Skills:

• Critical Thinking: The ability to analyze and evaluate information to make informed decisions.
• Problem-Solving: Identifying and resolving issues effectively and creatively.
• Communication Skills: The capability to communicate effectively with the team and stakeholders.
• Collaboration and Teamwork: Working harmoniously with a team to achieve common goals.
• Data Storytelling: Transforming data into understandable and meaningful stories.
• Presentation Skills: Delivering information and results clearly and engagingly.
• Adaptability: The ability to adjust to new changes and challenges.

Machine Learning:

• Supervised Learning: Training models using known data to make accurate predictions.
• Linear Regression: A predictive model that shows the relationship between variables.
• Logistic Regression: Used to predict binary outcomes (yes/no).
• Decision Trees: A model that uses a decision tree to classify data.
• Unsupervised Learning: Learning from data without specific labels.
• K-Means Clustering: Grouping data into clusters based on common characteristics.
• Hierarchical Clustering: Building a hierarchy of clusters.
• Model Evaluation: Measuring model performance using various metrics.
• Confusion Matrix: A tool for evaluating the performance of a classification model.
• ROC Curve: A graphical representation used to assess the performance of a classification model.

Data Visualization:

• Plotly: A library for creating interactive graphs.
• Seaborn: A data visualization library based on Matplotlib.
• Bokeh: A tool for creating interactive data visualizations.
• Taipy: A library for simplifying data analysis and visualization processes.
• Looker: A platform for data analysis and visualization.
• Matplotlib: A robust library for creating static charts.
• Tableau: Software for visualizing data and creating business dashboards.
• Power BI: A Microsoft tool for visualizing and presenting data.

Data Wrangling:

• Handling Missing Values: Dealing with incomplete data in a scientific manner.
• Data Transformation: Changing the format of data to make it analyzable.
• Data Cleaning: Preparing data for analysis by cleaning and organizing it.

SQL:

• Basics: Basic commands for managing databases, such as retrieving, inserting, updating, and deleting data.
• Subqueries: Merging tables and complex data queries.
• Window Functions: Analyzing data within a specific range of records.

Python:

• Pandas: A library for data manipulation and analysis.
• NumPy: A library for handling arrays and mathematical operations.
• Matplotlib: A library for plotting graphs.
• Seaborn: A library built on Matplotlib for data visualization.
• Scikit-learn: A library for machine learning.
• Plotly: A library for creating interactive charts.
• TensorFlow: A framework for building deep learning models.
• PyTorch: A framework for deep learning.

Mathematics & Statistics:

• Probability: The study of probabilities and related statistics.
• Hypothesis Testing: Testing scientific hypotheses using data.
• Linear Algebra: Dealing with vectors and matrices.
• Calculus: The study of rates of change.
• Descriptive Statistics: Describing and summarizing data.
• Inferential Statistics: Drawing conclusions from data.
• Statistical Analysis: Applying statistical methods to analyze data.