Understanding the Step-by-Step Data Science Process for Beginners | IABAC
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The data science process involves collecting, cleaning, analyzing, and interpreting data to find insights. It includes defining the problem, preparing data, building models, and communicating results to support decision-making and solve real-world business or research problems.
Understanding the Step-by-Step Data Science Process for Beginners | IABAC
- 2. • • • • • • Introduction to Data Science Understanding the Problem Data Collection Data Cleaning and Preparation Data Analysis and Modeling Communicating Results Agenda www.iabac.org
- 3. Introduction to Data Science ● ● ● Data Science is an interdisciplinary field focusing on extracting knowledge from data using various scientific methods, processes, algorithms, and systems. Applications span across multiple sectors including healthcare (predictive analytics), finance (fraud detection), retail (customer behavior analysis), and technology (recommendation systems). It plays a critical role in modern industries by enabling data-driven decision-making and providing insights that drive strategic actions. Overview of Data Science www.iabac.org
- 4. Understanding the Problem Key Elements ● ● ● ● Set specific, measurable goals and objectives to guide the project. Identify the key questions that need to be answered through data analysis. Engage stakeholders to gather insights and ensure alignment with business needs. Define the business problem clearly, understanding the context and impact. www.iabac.org
- 5. Data Collection Methods and Sources of Data Collection ● ● ● ● ● Data can be collected from primary sources such as surveys, interviews, and experiments. Data integration combines data from multiple sources to provide a comprehensive dataset. Ensuring data quality and relevance is crucial during the collection process. Secondary data sources include existing databases, public records, and internet data. Automated data collection involves web scraping, APIs, and IoT devices to gather real-time data. www.iabac.org
- 6. Data Cleaning and Preparation ● ● ● ● ● Detect and address outliers that can skew analysis results. Identify and remove duplicates to ensure unique data entries. Correct inconsistencies in data formats, such as date formats or categorical values. Handle missing values by imputation, deletion, or using algorithms that support missing data. Normalize data to a consistent scale, especially for algorithms that are sensitive to data ranges. Steps in Data Cleaning and Preparation www.iabac.org
- 7. Data Analysis and Modeling Techniques and Methods Exploratory Data Analysis (EDA) includes summarizing main characteristics of the data using visualizations. Regression analysis is used for predicting a continuous outcome variable based on one or more predictor variables. Classification techniques such as decision trees, random forests, and support vector machines are used for predicting categorical outcomes. Clustering methods like k-means and hierarchical clustering group similar data points together. Neural networks and deep learning models are used for handling complex patterns and large datasets. www.iabac.org
- 8. Communicating Results Effective Communication Techniques ● ● ● ● Clear and concise reporting ensures that the results are communicated without ambiguity, fostering informed decision-making. Interactive dashboards allow stakeholders to explore data dynamically, enhancing engagement and understanding of key metrics. Data visualization transforms complex data into understandable visual formats, making insights accessible to a broader audience. Storytelling with data involves crafting a narrative around the data insights, providing context and relevance to the findings. www.iabac.org