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What is Data Science and How to Start a Career in It - IABAC

Data science is the practice of analyzing large amounts of data using statistics, programming, and machine learning. It helps uncover patterns, make predictions, and support decision-making in areas like business, healthcare, finance, and technology.

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What is Data Science and How to Start a Career in It - IABAC

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  1. What is Data Science? www.iabac.org

  2. Agenda • • • • • • Introduction to Data Science Key Components of Data Science Data Science Lifecycle Skills Required for Data Scientists Applications of Data Science Future Trends in Data Science www.iabac.org

  3. Introduction to Data Science Data science is an interdisciplinary field that utilizes various techniques, algorithms, and systems to extract meaningful insights and knowledge from structured and unstructured data. It combines elements from statistics, computer science, and domain expertise to analyze and interpret complex data sets, enabling informed decision-making and predictive analytics. At its core, data science encompasses data collection, data processing, and data visualization to transform raw data www.iabac.org

  4. Key Components of Data Science Data Collection Data Cleaning Gathering raw data from various sources, including databases, web scraping, sensors, and manual input. Ensuring data is relevant and sufficient for analysis. Removing or correcting errors, dealing with missing values, and standardizing formats. This step is crucial for ensuring data quality and accuracy. Data Analysis Data Visualization Applying statistical methods and algorithms to extract insights, identify patterns, and make predictions. This forms the core of data-driven decision-making. Creating charts, graphs, and dashboards to present data findings in an easily understandable way. Helps www.iabac.org

  5. Data Science Lifecycle Data Collection Data Preparation Data Analysis Model Building Cleaning and transforming raw data to make it suitable for analysis. This includes handling missing values, outliers, and normalization. Exploring the prepared data to find patterns, correlations, and insights using statistical methods and visualization tools. Developing predictive models using machine learning algorithms. This step involves training, validating, and tuning models to optimize Gathering raw data from various sources such as databases, APIs, and web scraping to comprehensive dataset. ensure a Tprearifnoerdm aMnocdee.ls Validation Results Model Performance Metrics Raw Data Files APIs Database Exports Cleaned Data Transformed Data Files Data Quality Reports Descriptive Statistics Data Visualizations Insight Reports www.iabac.org

  6. Skills Required for Data Scientists 1 2 3 Proficiency in programming languages like Python and R is essential for implementing algorithms and processing data. Strong understanding of statistics to perform hypothesis testing, regression analysis, and statistical modeling. Knowledge of machine learning techniques for predictive modeling and pattern recognition. 4 5 6 Data wrangling skills to clean, transform, and organize raw data into usable formats. Expertise in data visualization tools like Tableau and Matplotlib to present findings clearly and effectively. Ability to communicate complex technical results to non-technical stakeholders in a comprehensible manner. www.iabac.org

  7. Applications of Data Science Healthcare Predictive analytics for patient outcomes and personalized treatment plans. Finance Fraud detection and algorithmic trading using large datasets. Marketing Customer segmentation and targeted advertising campaigns. www.iabac.org

  8. Future Trends in Data Science AI Integration: Increasing use of AI to automate data preprocessing, analysis, and interpretation, enhancing efficiency and accuracy. 1 Automated Machine Learning (AutoML): Tools that simplify model selection, hyperparameter tuning, and deployment, making data science more accessible. 2 Edge Computing: Shift towards processing data closer to the source to reduce latency and improve real-time analytics. 3 www.iabac.org

  9. Thank You www.iabac.org

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