We believe that you also went through Python and SQL basics. If you need to learn more about Python and SQL check the tutorials below:
Python Tutorial for Beginners: https://youtu.be/kqtD5dpn9C8?si=NqhFit3WyrrjQGQK
SQL for Data Reporting and analisys Tutorial for Begginers: https://youtu.be/h0nxCDiD-zg?si=P5ffwij5dMNsxhxt
Both courses will take you less than 2 hours to complete please make an effort and go through them.
Now, on Day 2:
We are going to explore more about the Data Science Lifecycle or Processes, understand each tool used at each stage, and best practices.
The data science lifecycle or process is a structured approach that data scientists follow to solve complex problems and extract valuable insights from data. It typically consists of several stages, which may vary slightly depending on the specific framework or methodology being used.
1). Problem Definition:
- Identify and clearly define the problem or business question that needs to be addressed.
- Establish project goals and objectives.
- Define success criteria and key performance indicators (KPIs).
2). Data Collection:
- Gather and collect relevant data from various sources, including databases, APIs, web scraping, or external datasets.
- Ensure data quality by handling missing values, outliers, and inconsistencies.
- Organize and store data in a suitable format for analysis.
3). Data Exploration and Understanding:
- Perform initial data exploration to gain insights into the data.
- Visualize data through charts, graphs, and statistical summaries.
- Identify patterns, trends, and relationships within the data.
- Formulate hypotheses about the data.
4). Data Preprocessing:
- Clean and preprocess the data by addressing issues such as data normalization, scaling, and feature engineering.
- Handle categorical variables through encoding techniques.
- Split the data into training and testing sets for model evaluation.
5). Model Selection and Training:
- Choose appropriate machine learning algorithms or statistical models based on the problem and data characteristics.
- Train multiple models and tune hyperparameters to optimize model performance.
- Evaluate models using appropriate metrics (e.g., accuracy, precision, recall, F1-score, etc.).
- Perform cross-validation to ensure robustness of the models.
6) Model Evaluation:
- Assess model performance on the testing dataset.
- Utilize various evaluation techniques such as confusion matrices, ROC curves, and cross-validation to gauge model effectiveness.
- Compare different models and select the best-performing one.
7). Model Deployment:
- Prepare the model for deployment in a production environment.
- Implement necessary software and infrastructure to make the model accessible to end-users.
- Monitor the model's performance and retrain it as needed.
8). Communication of Results:
- Summarize and present the findings in a clear and understandable manner.
- Create visualizations and reports to convey insights to stakeholders.
- Provide recommendations and actionable insights based on the analysis.
9). Feedback and Iteration:
- Gather feedback from stakeholders and end-users.
- Incorporate feedback into the model or analysis to improve results.
- Iterate through the process as necessary to address new questions or issues.
10). Documentation and Knowledge Sharing:
- Document all aspects of the project, including data sources, preprocessing steps, models used, and results obtained. _ Share knowledge and insights with the team and organization to promote learning and future improvements.
The data science lifecycle is iterative, and data scientists may revisit earlier stages as needed to refine their models or address changing requirements. Effective communication and collaboration with domain experts and stakeholders are crucial throughout the process to ensure the success of data science projects.
More resources:
How to Effectively Use the Data Science Lifecycle:
What is Data Science Project Life Cycle Explained Step by Step: