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    • Complete Data Science Training with Python for Data Analysis

    Complete Data Science Training with Python for Data Analysis

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    Udemy

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    Mode

    Online

    Fees

    ₹ 599 3699

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 599  ₹3,699
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction to the Data Science in Python Bootcamp

    • What is Data Science?
    • Introduction to the Course & Instructor
    • Data For the Course
    • Introduction to the Python Data Science Tool
    • For Mac Users
    • Introduction to the Python Data Science Environment
    • Some Miscellaneous IPython Usage Facts
    • Online iPython Interpreter
    • Conclusion to Section 1

    Introduction to Python Pre-Requisites for Data Science

    • Rationale Behind This Section
    • Different Types of Data Used in Statistical & ML Analysis
    • Different Types of Data Used Programatically
    • Python Data Science Packages To Be Used
    • Conclusions to Section 2

    Introduction to Numpy

    • Numpy: Introduction
    • Create Numpy Arrays
    • Numpy Operations
    • Matrix Arithmetic and Linear Systems
    • Numpy for Basic Vector Arithmetric
    • Numpy for Basic Matrix Arithmetic
    • Broadcasting with Numpy
    • Solve Equations with Numpy
    • Numpy for Statistical Operation
    • Conclusion to Section 3
    • Section 3 Quiz

    Introduction to Pandas

    • Data Structures in Python
    • Read in Data
    • Read in CSV Data Using Pandas
    • Read in Excel Data Using Pandas
    • Reading in JSON Data
    • Read in HTML Data
    • Conclusion to Section 4

    Data Pre-Processing/Wrangling

    • Rationale behind this section
    • Removing NAs/No Values From Our Data
    • Basic Data Handling: Starting with Conditional Data Selection
    • Drop Column/Row
    • Subset and Index Data
    • Basic Data Grouping Based on Qualitative Attributes
    • Crosstabulation
    • Reshaping
    • Pivoting
    • Rank and Sort Data
    • Concatenate
    • Merging and Joining Data Frames
    • Conclusion to Section 5

    Introduction to Data Visualizations

    • What is Data Visualization?
    • Some Theoretical Principles Behind Data Visualization
    • Histograms-Visualize the Distribution of Continuous Numerical Variables
    • Boxplots-Visualize the Distribution of Continuous Numerical Variables
    • Scatter Plot-Visualize the Relationship Between 2 Continuous Variables
    • Barplot
    • Pie Chart
    • Line Chart
    • Conclusions to Section 6

    Statistical Data Analysis-Basic

    • What is Statistical Data Analysis?
    • Some Pointers on Collecting Data for Statistical Studies
    • Some Pointers on Exploring Quantitative Data
    • Explore the Quantitative Data: Descriptive Statistics
    • Grouping & Summarizing Data by Categories
    • Visualize Descriptive Statistics-Boxplots
    • Common Terms Relating to Descriptive Statistics
    • Data Distribution- Normal Distribution
    • Check for Normal Distribution
    • Standard Normal Distribution and Z-scores
    • Confidence Interval-Theory
    • Confidence Interval-Calculation
    • Conclusions to Section 7

    Statistical Inference & Relationship Between Variables

    • What is Hypothesis Testing?
    • Test the Difference Between Two Groups
    • Test the Difference Between More Than Two Groups
    • Explore the Relationship Between Two Quantitative Variables
    • Correlation Analysis
    • Linear Regression-Theory
    • Linear Regression-Implementation in Python
    • Conditions of Linear Regression
    • Conditions of Linear Regression-Check in Python
    • Polynomial Regression
    • GLM: Generalized Linear Model
    • Logistic Regression
    • Conclusions to Section 8
    • Section 8 Quiz

    Machine Learning for Data Science

    • How is Machine Learning Different from Statistical Data Analysis?
    • What is Machine Learning (ML) About? Some Theoretical Pointers

    Unsupervised Learning in Python

    • Unsupervised Classification- Some Basic Ideas
    • KMeans-theory
    • KMeans-implementation on the iris data
    • Quantifying KMeans Clustering Performance
    • KMeans Clustering with Real Data
    • How Do We Select the Number of Clusters?
    • Hierarchical Clustering-theory
    • Hierarchical Clustering-practical
    • Principal Component Analysis (PCA)-Theory
    • Principal Component Analysis (PCA)-Practical Implementation
    • Conclusions to Section 10

    Supervised Learning

    • What is This Section About?
    • Data Preparation for Supervised Learning
    • Pointers on Evaluating the Accuracy of Classification and Regression Modelling
    • Using Logistic Regression as a Classification Model
    • RF-Classification
    • RF-Regression
    • SVM- Linear Classification
    • SVM- Non Linear Classification
    • Support Vector Regression
    • knn-Classification
    • knn-Regression
    • Gradient Boosting-classification
    • Gradient Boosting-regression
    • Voting Classifier
    • Conclusions to Section 11
    • Section 11 Quiz

    Artificial Neural Networks (ANN) and Deep Learning (DL)

    • Theory Behind ANN and DNN
    • Perceptrons for Binary Classification
    • Getting Started with ANN-binary classification
    • Multi-label classification with MLP
    • Regression with MLP
    • MLP with PCA on a Large Dataset
    • Start With Deep Neural Network (DNN)
    • Start with H20
    • Default H2O Deep Learning Algorithm
    • Specify the Activation Function
    • H2O Deep Learning For Predictions
    • Conclusions to Section 12
    • Section 12 Quiz

    Miscellaneous Lectures & Information

    • Data For This Section
    • Read in Data from Online CSV
    • Read Data from a Database
    • Data Imputation
    • Accessing Github

    Instructors

    Ms Minerva Singh

    Ms Minerva Singh
    Data Scientist
    Udemy

    Other Masters, Ph.D, M.Phil.

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