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What are the main topics in Data Science | IABAC

Data Science covers key topics like data collection, data cleaning, exploratory analysis, statistics, machine learning, deep learning, natural language processing, data visualization, time series forecasting, and model deploymentu2014blending programming, math, and domain expertise to extract insights and drive decisions.

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What are the main topics in Data Science | IABAC

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  1. What are the main topics in Data Science? www.iabac.org

  2. Introduction to Data Science Data science is the field of extracting meaningful insights from large volumes of data using methods from statistics, machine learning, and programming. It helps businesses and researchers make informed decisions, predict trends, automate processes, and solve complex problems across various industries by turning raw data into actionable knowledge. www.iabac.org

  3. Data Collection & Cleaning Collect data via APIs, web scraping, databases Ensure data quality, consistency & format Tools: Python (Pandas), OpenRefine, SQL First crucial step in any data project www.iabac.org

  4. Exploratory Data Analysis (EDA) Understand trends, patterns, outliers Use statistics & visual tools (Seaborn, Matplotlib) Informs feature selection & modeling Helps refine business questions. www.iabac.org

  5. Statistics & Probability Foundation of all analysis Techniques: hypothesis testing, confidence intervals Used in A/B testing, survey analysis Enables valid conclusions from data www.iabac.org

  6. Machine Learning (ML) Core technique in data science Supervised, unsupervised & ensemble learning Tools: Scikit-learn, XGBoost, LightGBM Applications: recommendation, classification, prediction www.iabac.org

  7. Deep Learning & NLP Deep Learning: CNNs, RNNs, Transformers NLP: Sentiment analysis, topic modeling, chatbots Libraries: TensorFlow, PyTorch, BERT Used in AI, image, text & speech analysis www.iabac.org

  8. Time Series & Forecasting Analyze data over time Tools: ARIMA, Prophet, LSTM Applications: demand forecasting, stock trends Handle seasonality & temporal patterns www.iabac.org

  9. Data Visualization & Communication Tell data stories clearly Tools: Power BI, Tableau, Plotly Key skill for stakeholder buy-in Design accessible, clear visualizations www.iabac.org

  10. Deployment & Cloud Tools Move models to production (Flask, Docker) Use CI/CD for automation Platforms: AWS, Azure, GCP MLOps for scalable & monitored systems www.iabac.org

  11. Thank You www.iabac.org

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