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Data Science Essential - DSE

  • Category: Software Development
  • Exam Code: DSE
  • Type of Question: Multiple-choice question
  • Exam Duration: 120 Minutes
  • Passing Score: 60%
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A thorough grasp of statistical analysis, familiarity with huge dataset processing, and programming languages like Python or R are all part of the Data Science Essentials. To extract important insights and make wise judgments, data scientists use machine learning algorithms and data visualization approaches. Gaining an understanding of Data Science Essentials is essential for generating actionable insight and promoting innovation in a variety of sectors, since it centers on the extraction of knowledge from raw data

Course Curriculum

  1. Understanding Data Science
    • Definition and scope of data science
    • Data science lifecycle and processes
    • Role of a Data Scientist
  2. Data Exploration and Preprocessing
    • Exploratory Data Analysis (EDA) techniques
    • Handling missing data and outliers
    • Data cleaning and transformation
  3. Introduction to Data Visualization
    • Importance of data visualization
    • Common visualization tools and libraries (e.g., Matplotlib, Seaborn)
    • Creating effective visualizations
  1. Statistical Analysis for Data Science
    • Descriptive and inferential statistics
    • Hypothesis testing
    • Correlation and Regression Analysis
  2. Introduction to Machine Learning
    • Overview of machine learning concepts
    • Types of Machine Learning Algorithms (supervised, unsupervised, and reinforcement learning)
    • Model training, evaluation, and prediction
  3. Project: Exploratory Data Analysis and Visualization
    • Applying EDA, data preprocessing, and visualization to a real-world dataset
  1. Regression Analysis
    • Linear and non-linear regression
    • Model evaluation metrics for regression
    • Practical applications of regression
  2. Classification Algorithms
    • Basics of classification problems
    • Popular Classification Algorithms (e.g., Decision Trees, SVM)
    • Model evaluation and metrics for classification
  3. Project: Building a Supervised Learning Model
    • Implementing a supervised learning model on a provided dataset
  1. Clustering Techniques
    • K-Means clustering
    • Hierarchical clustering
    • Use cases and applications
  2. Dimensionality Reduction
    • Principal Component Analysis (PCA)
    • t-Distributed Stochastic Neighbor Embedding (t-SNE)
    • Feature selection and extraction
  3. Project: Unsupervised Learning and Feature Engineering
    • Applying clustering and dimensionality reduction on a real-world dataset
  1. Time Series Analysis
    • Basics of time series data
    • Time series visualization and decomposition
    • Forecasting techniques
  2. Natural Language Processing (NLP)
    • Introduction to Text Data
    • Text preprocessing and tokenization
    • Building basic NLP models
  3. Capstone Project: Real-world Data Science Application
    • Comprehensive project integrating various data science concepts on a larger dataset