Data Science Foundation with Python (Data Science Foundation with Python)

Data science is a interdisciplinary field of computers, statistics, and critical thinking. The course starts with the basics introduction to statistical framework using the NumPy and pandas. With brief introduction to forecasting and machine learning.

What Will I Learn ?

  • Python Introduction
  • Numerical Computation
  • Statistical Foundation
  • Exploratory Data Analysis
  • Data Processing Framework
  • Inferential Statistics
  • Data Analysis

Prerequisite Knowledge

This course people who are already adept with python who want to explore the world of data science.

Who can benefits ?

  • Ease of Learning
  • Faster Development and Processing
  • Powerful Packages
  • Community Support
  • Better Data Visualisation
  • Compatible with Hadoop

Opportunity Scope

Mentor shall discuss on classroom.

Modules / Chapter

Preliminaries

Quick introduction to python libraries, revision of structure and construct.

        Prerequisite:

    • Python Introduction
    • Python: Beyond basic

    Introduction

    • Python for Data Analysis
    • Essential Python Libraries

    python

    • Basic
      • Loops, Range, Enumerate
      • Functions and Lambda
      • Sets and Dictionaries
    • Primer
      • Iterator and Generators
      • Map, Filter, Reduce
      • List Comprehension

Numerical Computation

    introduction to numpy

    Prerequisite:

    • Python: Intro
    • Python: Beyond Basic

    Numerical Python

    • Why NumPy 
    • Operation Rules 
    • Function
    • Broadcasting
    • nd-array
      • Indexing: Normal and Fancy
      • Slicing Opertation
      • Reshaping, Pivoting, Transform

      Introduction to Matplotlib

    • Graphing Data
      • Scatter line, Stemplot
      • Bar: Histograms, Pareto Charts
      • Other: Pie Charts, Box Plots/Box
    • Introduction to Matplotlib 
      • Axis
      • Subplots
      • Color map
      • Styling

Statistical Foundation

    • Handling Data 
      • Web, html
      • Plain, Tabular
    • Basic Statistics
      • What is Data
        • Qualitative and Quantitative
        • Discrete and Continuous
        • Measurement: Nominal. Ordinal, Interval, Ratio
    • Data Summary
      • Measures of Center: Mean, Median, Mode, Midrange
      • Frequency Tables/Distributions
      • Class Limits, Width, Midpoints, Boundaries
      • Frequencies: Cumulative, Relative
      • Standard Deviation and Mean of Frequency Table

Exploratory Data Analysis

  • Quantitative Association
    • Covariance
    • Correlation
    • Scatter Matrix

Data Processing Framework

Pandas is the integrated structural framework for statistics, data manipulation analysis.

    Data Frame

    • Data Selection
      • Sorting, Searching, Group-By, filter
    • Data Summarization
      • Aggregation methods, Axis

    Time Series

    • Date and Time Module
      • Delta, Time Spans, Conversion
    • Series
      • Shifting, Resampling, Frequency
      • Rolling Statistics
      • Lag Function
      • Window Function

    Inferential Statistics

    • Sampling
      • Populations, Samples, Parameters
      • Random Samples and Simple Random Samples
      • Stratified, Cluster, Systematic, and Convenience Sampling
      • Sample Size needed to Estimate a Population Proportion
    • Measures of Relative Standing
      • Variance
      • Coefficient of Variation
      • Z-Scores
      • Maximum 

    • Probability

        • Probability Distributions
        • The Normal Distribution
          • Relationship Between Variables
          • Computing Normal Probabilities
          • Standard Normal Distribution
        • Central Limit Theorem
        • Binomial Distribution
        • Poisson Distribution
      • Precision and Significance
        • Interpreting Confidence Intervals
        • Margin of Error
        • Z versus T
      • NHST
        • Hypothesis Tests for one mean, two means
        • One proportion, two proportions
        • Dependent and independent data
        • Hypothesis tests given the data
        • Hypothesis tests for paired Data
        • Interpreting Hypothesis tests
        • Computing p-values

    Data Analysis

      • Association of variable
        • Correlation
        • Serial correlation test: durbin watson test
        • Chi-square Test
        • Analysis of Variance
      • Correlation Analysis
        • Linear Correlation
          • Coefficient R
          • Coefficient of Determination
          • Least Squares Regression Line
      • Regression
        • Simple Linear Regression
          • Least Squares Line
          • Correlation Coefficient
          • Coefficient of Determination
        • General Linear Model
          • f-test
        • Multicollinarity
        • Multi Variable Linear Regression
      • Time Series Analysis
        • Self Correlation
          • Auto Correlation Function
          • Partial Autocorrelation
        • Trend Analysis
          • Detrend, Sesonality
          • Stationarity Tests: Dickey Fuller Test
          • Forecasting Using ARIMA Model

    Enquiry Form

    Required fields are marked (*).

    (Max 350 words only)

    Contact Information

    • Address

      Anamnagar - 32 Kathmandu, Nepal

    • Email

      info@labanepal.com

    • Phone

      +977-1-4102721, 4102722, 4244804

    • Opening Hours

      10 AM - 5 PM

    Registration Form

    Required fields are marked (*).

    (Max 350 words only)

    Contact Information

    • Address

      Anamnagar - 32 Kathmandu, Nepal

    • Email

      info@labanepal.com

    • Phone

      +977-1-4102721, 4102722, 4244804

    • Opening Hours

      10 AM - 5 PM

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