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Key points about this course

Duration : 3 Days
Course Fee : RM3,900.00

HRD Corp Claimable Course

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Data Analysis with Python
Exam Code : Not available
  • Beginner level
  • You should have a working knowledge of Python and Jupyter Notebooks
  • Class led by a trainer who is an expert in Python

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Public Class

In-House Training

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Course Overview

Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models.

 

Topics covered include:

  • collecting and importing data
  • cleaning, preparing & formatting data
  • data frame manipulation - summarizing data,
  • building machine learning regression models
  • model refinement
  • creating data pipelines

 

You will learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations.

You will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them. In addition to video lectures you will learn and practice using hands-on labs and projects.

You will work with several open source Python libraries, including Pandas and Numpy to load, manipulate, analyze, and visualize cool datasets. You will also work with scipy and scikit-learn, to build machine learning models and make predictions.

Course Prerequisites

You should have a working knowledge of Python and Jupyter Notebooks.

Course Objectives

You will learn

  • Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data
  • Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy
  • Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines
  • Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making
Course Content

 

Module 1: Importing Datasets

In this module, you will learn how to understand data and learn about how to use the libraries in Python to help you import data from multiple sources. You will then learn how to perform some basic tasks to start exploring and analyzing the imported data set.

  • The Problem
  • Understanding the Data
  • Python Packages for Data Science
  • Importing and Exporting Data in Python
  • Getting Started Analyzing Data in Python

 

Module 2: Data Wrangling

In this module, you will learn how to perform some fundamental data wrangling tasks that, together, form the pre-processing phase of data analysis. These tasks include handling missing values in data, formatting data to standardize it and make it consistent, normalizing data, grouping data values into bins, and converting categorical variables into numerical quantitative variables.

  • Pre-processing Data in Python
  • Dealing with Missing Values in Python
  • Data Formatting in Python
  • Data Normalization in Python
  • Binning in Python
  • Turning categorical variables into quantitative variables in Python

 

Module 3: Exploratory Data Analysis

In this module, you will learn what is meant by exploratory data analysis, and you will learn how to perform computations on the data to calculate basic descriptive statistical information, such as mean, median, mode, and quartile values, and use that information to better understand the distribution of the data. You will learn about putting your data into groups to help you visualize the data better, you will learn how to use the Pearson correlation method to compare two continuous numerical variables, and you will learn how to use the Chi-square test to find the association between two categorical variables and how to interpret them.

  • Exploratory Data Analysis
  • Descriptive Statistics
  • GroupBy in Python
  • Correlation
  • Correlation – Statistics
  • Analysis of Variance ANOVA

 

Module 4: Model Development

In this module, you will learn how to define the explanatory variable and the response variable and understand the differences between the simple linear regression and multiple linear regression models. You will learn how to evaluate a model using visualization and learn about polynomial regression and pipelines. You will also learn how to interpret and use the R-squared and the mean square error measures to perform in-sample evaluations to numerically evaluate our model. And lastly, you will learn about prediction and decision making when determining if our model is correct.

  • Model Development
  • Linear Regression and Multiple Linear Regression
  • Model Evaluation using Visualization
  • Polynomial Regression and Pipelines
  • Measures for In-Sample Evaluation
  • Prediction and Decision Making

 

Module 5: Model Evaluation

In this module, you will learn about the importance of model evaluation and discuss different data model refinement techniques. You will learn about model selection and how to identify overfitting and underfitting in a predictive model. You will also learn about using Ridge Regression to regularize and reduce standard errors to prevent overfitting a regression model and how to use the Grid Search method to tune the hyperparameters of an estimator.

  • Model Evaluation and Refinement
  • Overfitting, Underfitting and Model Selection
  • Ridge Regression
  • Grid Search

  • Data Analysis with Python

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