Data Analytics Using Google CoLab : A course for Beginners

Name: Data Analytics Using Google CoLab : A course for Beginners
Rating: 3.7 (12 reviews)

Students will learn how to code different statistical models and machine learning models using google cloud lab

Created byRituraj Dixit

Last updated 2/2022

English

English [Auto],

What you'll learn

Problem solving skills related to data
Detecting hidden pattern in the data
Building Predictive models for different domains
Basic concepts of Business Statistics and Machine Learning
Advance statistical concepts used in business analytics and data analytics
Time Series Forecasting
Building Recommendation System
Quiz on each section for evaluation

Course content

14 sections • 75 lectures • 20h 38m total length

Introduction to Data Analytics using Google CoLab: An overview15:05
This lecture will give detail outline of the course and the outcomes which will be there after completion of the course
The outcomes of the course and future prospects as data analyst ...6:17
Explore the course outcomes and future prospects as a data analyst, with hands-on learning in Google Colab, Python, statistics, sampling, hypothesis testing, regression, clustering, and data cleaning.
The detail course structure of section 14:14
Explore Python fundamentals, data structures, and functions in Google Colab; learn pandas and NumPy basics, and visualize data with histograms and scatter plots, while saving notebooks to Google Drive.
How to link a .csv file with Google Colab7:31
Learn to connect a CSV file to Google Colab by linking Google Drive, obtaining a shareable link, and running a prepared code snippet to read data into a pandas DataFrame.
Basics of Python : Part19:11
This lecture gives basic programming knowledge of python along with their programming constructs, the conditional statements like IF Else will be introduced along with examples
Basics of Python : Part2(Loops in python)9:11
The concepts of for loop and while loops are explained in this lecture
Basics of Python : Part3(List)24:07
The concepts of List is explained in this lecture which will help students in building the model
Basics of Python : Part4 (Dictionary and tuples)15:21
Dictionary are very important constructs in building data models so they are explained along with tuples
Basics of Python : Part5 (Functions in python)17:07
Concepts of Numpy in Google Colab41:54
Explore numpy basics in Google Colab, including arrays, shapes, slicing and indexing, transpose, dot product, zeros, identity matrices, and linspace, with practical Python examples.
Concepts of Pandas part19:22
Master the basics of numpy and pandas for data frames in Google Colab, including selecting columns with iloc and loc, understanding data types, and working with series.
Concepts of Pandas part212:49
Explore pandas concepts for data analysis, including max, min, mean, and describe, plus head, tail, shape, and value_counts. Learn to filter, query, and select columns in dataframes and inspect distributions.
Exploratory data analysis using test case : part114:43
Explore exploratory data analysis and data cleaning using google colab, build a logistic regression model with train/test split, and visualize data with seaborn to predict car prices.
Quiz Of Section 1

Probability Distribution Functions and Probability Mass Functions15:26
Explore probability distribution concepts, including empirical and skewed distributions, and learn how PDFs and PMFs describe probabilities for continuous and discrete variables using Python in Google Colab.
Python implementation of Different type of Distribution6:14
Cumulative Distribution Functions20:09
Questions on Probability Distribution21:41
Concepts of Bayes Theorem part125:02
Learn the fundamentals of Bayesian learning, conditional probability, and Bayes rule. Build and compare Bayesian classifiers and networks with practical spam/ham and cancer data examples.
Concept of Bayes Theorem an Naïve Bayes Classifier6:59
Naïve Bayes theorem Part 314:25
Practical Example of Bayes Theorem13:35
In this lecture Social_Network_Ads data set has been used to implement the naive bayes classifier using pre defines classes of sklearn
Building a Naïve Bayes Classifier from scratch20:18
Build a naïve bayes classifier from scratch by computing word probabilities from training data and using spam versus ham likelihood to classify messages.
Quiz for section 3

Overview of Section and its outcomes7:30
This video will the learner about the outcome of ti section that means what he will gain after completing the course
Concept of Sampling Theory and Distribution19:25
The concept of sampling theory is explained in great detail along with practical exercises, also focusses on different types of sampling in the statistics, it is very important to understand the sampling theory for a data analyst
Confidence Interval Estimation30:06
Learn to distinguish point estimation from confidence interval estimation, construct population mean intervals using z, interpret p-values and z-values for hypothesis testing, and assess estimator bias.
Quiz on section 4

Concept of Linear and Logistic Regression18:39
Learn linear regression by fitting a line to population and ice cream sales, minimizing squared error with gradient updates to beta_0 and beta_1, and introduce logistic regression with a sigmoid.
Implementation of linear regression from scratch16:22
An example of Ordinary Least Square7:47
Multivariate Linear Regression24:45
Explore multivariate linear regression with multiple features, estimating travel time from distance and deliveries via least squares. Assess fit with coefficient of determination and tests for coefficients in trucking data.
Significance of independent vs dependent variable11:42
Multivariate Linear regression implementation in Python20:42
Maximum Likelihood estimation in Statistics23:59
Maximum likelihood estimation explains how to maximize the likelihood of observed data using pdf or pmf, with log-likelihood for easier differentiation and parameter estimation.
Concepts of Logistic Regression in Business Statistics10:30
Learn how logistic regression uses the sigmoid function for binary classifications, estimating loan and credit card approval probabilities, and assess variable significance with chi-square and feature insights.
Implementation of Logistic Regression in python18:37
Implement logistic regression in Python on Google Colab, using normalized spending and card membership to predict coupon usage, and apply maximum likelihood estimation with a classification report.
Loan Prediction algorithm implementation using Logistic Regression32:05
Explore loan prediction using logistic regression in Google Colab, emphasizing data understanding, feature engineering, handling missing values, and evaluating model performance with training and test data.
Quiz on section 6

Feature engineering on house price prediction22:03
Explore feature engineering for house price prediction using google colab, separating continuous and categorical features, handling nulls, imputing or dropping data, and selecting significant predictors for a linear regression model.
Feature Engineering on Categorical Variable : House Prediction12:51
Heart Disease Prediction using Feature Engineering14:49
The heart disease data which we have used can be found easily in Kaggle. We have build an improved hear disease classifier by implementing Logistic Regression Classifier separately first with continuous data second with categorical data and then merging the two of them, let u s see what happen and how we did it
Quiz on Section 7

Overview of Decision Tree and its implementation7:00
Explore how decision trees act as powerful classifiers and regressors, using entropy and information gain to build roots and nodes, and compare pruning, depth, and Gini impurity in Python.
Introduction to Decision tree and ID323:41
Learn how decision trees organize data into internal and leaf nodes, using entropy and information gain to split on attributes and build pruned, robust models like ID3 and C4.5.
Introduction to ID3 and its concepts21:30
Concept of Entropy, Overfitting and Information Gain13:31
Learn how entropy and information gain guide decision tree splits, examine hypothesis space and model selection, and apply pruning and regularization to combat overfitting.
Practical Implementation of ID3and their limitations16:10
Explore the practical implementation of the ID3 decision tree for nominal data, using entropy and information gain, and note limitations like no pruning and potential overfitting, with references to C4.5.
C4.5 Decision and its advancement over ID3 Decision tree6:50
Explore the C4.5 decision tree and its improvements over ID3, using entropy-based information gain to handle categorical data within a supervised learning framework.
Building a Decision tree with data using python from scratch22:51
Learn to build a decision tree from scratch in Python on a small dataset, using information gain and entropy to choose splits and assess model performance.
Practical Implementation of CART Algorithm using Python27:19
Learn practical implementation of the cart decision tree algorithm in Python, including gini impurity, information gain, and splitting rules with color and diameter features to classify fruits.
CHAID Algorithm and its importance in Data Analytics22:58
Implementation of CHAID using Python10:34
Implement CHAID in Python on Google Colab to build and evaluate decision trees using entropy, gain, and gain ratio, with comparisons to Gini index and CART concepts.
Random Forest Regressor and its comparison with Linear Regression model -part35:44
Quiz on Section 9

Requirements

Be able to code and love for data
No such specific prerequisites

Description

In this course we have examples of analytics in a wide variety of industries, and we expect that students will learn how you can use data analytics in their career and become data analyst. One of the most important aspects of this course is that you, the student, are getting hands-on experience creating analytics data models. The course has four module first module give learner knowledge about python programming which include packages like Pandas, Numpy and Scipy are being taught in detail, second module introduces Business Statistics where students will get in depth knowledge of Descriptive Statistics, Inferential Statistics and Predictive Statistics along with their example in python i.e. how to implement all statistical modules in python, third module introduces to machine learning in which you will be introduced with Linear and Logistic Regression , Ordinary Least Squares, SVD and PCA for reducing dimensions of the data and the fourth module dedicated to implementation of learned ideas in projects where you were taught to work on data through four phases Data Discovery, Exploratory Data Analysis ,Model Building and result analysis. This is not an end you will going to have a free demo on "Building Movie Recommendation system from scratch" in Google CoLab.

Who this course is for:

Beginners willing to learn programming Business Statistics in Python
Beginners Python developers curious about analysis of data

Data Analytics Using Google CoLab : A course for Beginners

What you'll learn

Explore related topics

Course content

***Data Analytics from basic to advance***13 lectures • 3hr 7min

Data Wrangling : Clean, Transform, Merge and Reshape2 lectures • 31min

*** Probability and Probability Distributions***9 lectures • 2hr 24min

***Sampling Theory and Sampling Distribution***3 lectures • 57min

***Hypothesis Testing in business statistics***1 lecture • 27min

***Linear Regression and Logistic Regression using Python***10 lectures • 3hr 5min

***Feature Engineering In Business Statistics***3 lectures • 50min

***Singular Value Decomposition***1 lecture • 24min

***Decision Trees using python in Google Colab***11 lectures • 2hr 58min

***Time Series and Exploratory Data Analysis***3 lectures • 1hr 9min

Requirements

Description

Who this course is for: