Defining Data Science: A Comprehensive Overview

Defining Data Science: A Comprehensive Overview

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  What is DataScience? www.iabac.org
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  • • • • • • Introduction to DataScience Key Components of Data Science Data Science Lifecycle Skills Required for Data Scientists Applications of Data Science Future Trends in Data Science Agenda www.iabac.org
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  Introduction to DataScience 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
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  Gathering raw datafrom various sources, including databases, web scraping, sensors, and manual input. Ensuring data is relevant and sufficient for analysis. Applying statistical methods and algorithms to extract insights, identify patterns, and make predictions. This forms the core of data-driven decision-making. Removing or correcting errors, dealing with missing values, and standardizing formats. This step is crucial for ensuring data quality and accuracy. Creating charts, graphs, and dashboards to present data findings in an easily understandable way. Helps Data Analysis Data Collection Data Cleaning Data Visualization Key Components of Data Science www.iabac.org
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  Data Science Lifecycle DataCollection Data Preparation Data Analysis Model Building Raw Data Files APIs Database Exports Gathering raw data from various sources such as databases, APIs, and web scraping to ensure a comprehensive dataset. Cleaned Data Transformed Data Files Data Quality Reports Cleaning and transforming raw data to make it suitable for analysis. This includes handling missing values, outliers, and normalization. Descriptive Statistics Data Visualizations Insight Reports 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 Tprearifnoerdm aMnocdee.ls Validation Results Model Performance Metrics www.iabac.org
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  4 5 3 6 Proficiency inprogramming languages like Python and R is essential for implementing algorithms and processing data. 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. Strong understanding of statistics to perform hypothesis testing, regression analysis, and statistical modeling. Knowledge of machine learning techniques for predictive modeling and pattern recognition. Ability to communicate complex technical results to non-technical stakeholders in a comprehensible manner. Skills Required for Data Scientists 1 2 www.iabac.org
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  Applications of DataScience Customer segmentation and targeted advertising campaigns. Fraud detection and algorithmic trading using large datasets. Predictive analytics for patient outcomes and personalized treatment plans. Finance Marketing Healthcare www.iabac.org
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  2 1 3 AI Integration: Increasinguse of AI to automate data preprocessing, analysis, and interpretation, enhancing efficiency and accuracy. Automated Machine Learning (AutoML): Tools that simplify model selection, hyperparameter tuning, and deployment, making data science more accessible. Edge Computing: Shift towards processing data closer to the source to reduce latency and improve real-time analytics. Future Trends in Data Science www.iabac.org
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  www.iabac.org Thank You