4-in-1 Bundle covering the 4 essential topics for a data scientist - SQL, Tableau, Machine & Deep Learning using Python | Discount Coupon for Udemy Course
Published 4/2023Course Language EnglishCourse Caption English [Auto]Course Length 36:20:34 to be exact 130834 seconds!Number of Lectures 308
This course includes:
36.5 hours hours of on-demand video
5 article
Certificate of completion
Develop a strong foundation in SQL and understand how to use SQL queries to manipulate and retrieve data from a database.
Explore the features of Tableau and learn to create interactive visualizations to effectively communicate insights to stakeholders.
Master the concepts of machine learning and learn to implement various machine learning algorithms using Python.
Discover the basics of Deep Learning and understand how to build and train a deep neural network using Keras and TensorFlow.
Explore techniques for data preprocessing and feature engineering, including handling missing values and encoding categorical variables
Master the art of model selection and evaluation, including techniques for cross-validation, hyperparameter tuning, and overfitting prevention.
Discover the principles of deep neural networks and learn to build and train a convolutional neural network (CNN) for image classification.
Explore transfer learning and understand how to fine-tune a pre-trained CNN to solve a similar problem in a different domain.
If you are a curious learner looking to dive into the exciting world of data science, then this course is tailor-made for you! Do you want to master the essential skills required for a successful career in data science? Are you eager to develop expertise in SQL, Tableau, Machine and Deep Learning using Python? If your answer is a resounding "yes," then join us and embark on a journey towards becoming a data scientist!In this course, you will gain a comprehensive understanding of SQL, Tableau, Machine Learning, and Deep Learning using Python. You will develop the necessary skills to analyze data, visualize insights, build predictive models, and derive actionable business solutions. Here are some key benefits of this course:Develop mastery in SQL, Tableau, Machine & Deep Learning using PythonBuild strong foundations in data analysis, data visualization, and data modelingAcquire hands-on experience in working with real-world datasetsGain a deep understanding of the underlying concepts of Machine and Deep LearningLearn to build and train your own predictive models using PythonData science is a rapidly growing field, and there is a high demand for skilled professionals who can analyze data and provide valuable insights. By learning SQL, Tableau, Machine & Deep Learning using Python, you can unlock a world of career opportunities in data science, AI, and analytics.What's covered in this course?The analysis of data is not the main crux of analytics. It is the interpretation that helps provide insights after the application of analytical techniques that makes analytics such an important discipline. We have used the most popular analytics software tools which are SQL, Tableau and Python. This will aid the students who have no prior coding background to learn and implement Analytics and Machine Learning concepts to actually solve real-world problems of Data Science.Let me give you a brief overview of the coursePart 1 - SQL for data scienceIn the first section, i.e. SQL for data analytics, we will be teaching you everything in SQL that you will need for Data analysis in businesses. We will start with basic data operations like creating a table, retrieving data from a table etc. Later on, we will learn advanced topics like subqueries, Joins, data aggregation, and pattern matching.Part 2 - Data visualization using TableauIn this section, you will learn how to develop stunning dashboards, visualizations and insights that will allow you to explore, analyze and communicate your data effectively. You will master key Tableau concepts such as data blending, calculations, and mapping. By the end of this part, you will be able to create engaging visualizations that will enable you to make data-driven decisions confidently.Part 3 - Machine Learning using PythonIn this part, we will first give a crash course in python to get you started with this programming language. Then we will learn how to preprocess and prepare data before building a machine learning model. Once the data is ready, we will start building different regression and classification models such as Linear and logistic regression, decision trees, KNN, random forests etc.Part 4 - Deep Learning using PythonIn the last part, you will learn how to make neural networks to find complex patterns in data and make predictive models. We will also learn the concepts behind image recognition models and build a convolutional neural network for this purpose. Throughout the course, you will work on several activities such as:Building an SQL database and retrieving relevant data from itCreating interactive dashboards using TableauImplementing various Machine Learning algorithmsBuilding a Deep Learning model using Keras and TensorFlowThis course is unique because it covers the four essential topics for a data scientist, providing a comprehensive learning experience. You will learn from industry experts who have hands-on experience in data science and have worked with real-world datasets.What makes us qualified to teach you?The course is taught by Abhishek (MBA - FMS Delhi, B. Tech - IIT Roorkee) and Pukhraj (MBA - IIM Ahmedabad, B. Tech - IIT Roorkee). As managers in the Global Analytics Consulting firm, we have helped businesses solve their business problems using Analytics and we have used our experience to include the practical aspects of business analytics in this course. We have in-hand experience in Business Analysis.We are also the creators of some of the most popular online courses - with over 1,200,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet, or anything related to any topic, you can always post a question in the course or send us a direct message.Don't miss out on this opportunity to become a data scientist and unlock your full potential! Enroll now and start your journey towards a fulfilling career in data science.Who this course is for:Individuals who want to become data scientists or enhance their skills in data analysis, visualization, and modeling using SQL, Tableau, Machine Learning, and Deep Learning using Python.Professionals who want to upskill and add value to their existing roles by learning data scienceSmall business owners who want to use data to drive better decision-making in their companies
Course Content:
Sections are minimized for better readability, click the section title to view the course content
1 Lectures | 04:07
Introduction
04:07
4 Lectures | 15:08
Installing PostgreSQL and pgAdmin in your PC
10:44
This is a milestone!
03:31
If pgAdmin is not opening...
00:44
Course Resources
00:08
2 Lectures | 10:38
Case Study Part 1 - Business problems
04:21
Case Study Part 2 - How SQL is Used
06:17
11 Lectures | 01:12:31
CREATE
11:40
INSERT
09:07
Import data from File
04:59
SELECT statement
03:40
SELECT DISTINCT
06:05
WHERE
04:02
Logical Operators
06:03
UPDATE
05:24
DELETE
04:11
ALTER Part - 1
06:49
ALTER Part - 2
10:31
4 Lectures | 23:04
Restore and Back-up
07:37
Debugging restoration issues
08:26
Creating DB using CSV files
05:40
Debugging summary and Code for CSV files
01:21
3 Lectures | 18:50
IN
04:18
BETWEEN
05:40
LIKE
08:52
3 Lectures | 12:41
Side Lecture: Commenting in SQL
01:21
ORDER BY
07:42
LIMIT
03:38
1 Lectures | 03:33
AS
03:33
4 Lectures | 15:42
COUNT
05:07
SUM
03:24
AVERAGE
02:53
MIN & MAX
04:18
2 Lectures | 16:46
GROUP BY
11:42
HAVING
05:04
1 Lectures | 05:17
CASE WHEN
05:17
11 Lectures | 01:01:21
Introduction to Joins
02:54
Concepts of Joining and Combining Data
11:58
Preparing the data
02:00
Inner Join
08:04
Left Join
07:30
Right Join
06:27
Full Outer Join
04:59
Cross Join
04:21
Intersect and Intersect ALL
07:06
Except
02:53
Union
03:09
3 Lectures | 14:20
Subquery in WHERE clause
04:55
Subquery in FROM clause
05:23
Subquery in SELECT clause
04:02
2 Lectures | 13:39
VIEWS
07:14
INDEX
06:25
7 Lectures | 30:32
LENGTH
03:22
UPPER LOWER
02:10
REPLACE
04:13
TRIM, LTRIM, RTRIM
06:56
CONCATENATION
02:56
SUBSTRING
06:01
LIST AGGREGATION
04:54
5 Lectures | 17:20
CEIL & FLOOR
03:20
RANDOM
05:04
SETSEED
04:11
ROUND
02:27
POWER
02:18
3 Lectures | 16:31
CURRENT DATE & TIME
04:25
AGE
03:50
EXTRACT
08:16
3 Lectures | 22:51
PATTERN MATCHING BASICS
07:33
ADVANCE PATTERN MATCHING - Part 1
08:29
ADVANCE PATTERN MATCHING - Part 2
06:49
10 Lectures | 01:28:45
Introduction to Window functions
09:57
Introduction to Row number
06:04
Implementing Row number in SQL
19:19
RANK and DENSERANK
07:19
NTILE function
07:20
AVERAGE function
08:22
COUNT
03:55
SUM TOTAL
11:14
RUNNING TOTAL
06:58
LAG and LEAD
08:17
1 Lectures | 05:57
COALESCE function
05:57
2 Lectures | 16:35
Converting Numbers/ Date to String
10:46
Converting String to Numbers/ Date
05:49
2 Lectures | 13:12
User Access Control - Part 1
07:50
User Access Control - Part 2
05:22
4 Lectures | 20:05
Tablespace
05:37
PRIMARY KEY & FOREIGN KEY
05:02
ACID compliance
05:32
Truncate
03:54
2 Lectures | 14:02
Why Tableau
04:41
Tableau Products
09:21
4 Lectures | 31:51
Installing Tableau desktop and Public
05:02
About the data
09:59
Connecting to data
12:26
Live vs Extract
04:24
7 Lectures | 54:56
Combining data from multiple tables
04:27
Relationships in Tableau
13:56
Joins in Tableau
06:43
Types of Joins in Tableau
06:12
Union in Tableau
07:55
Physical Logical layer and Data models
06:30
The visualization screen - Sheet
09:13
3 Lectures | 22:23
Types of Data - Dimensions and Measures
06:49
Types of Data - Discreet and Continuous
06:09
Changing Data type in Tableau
09:25
3 Lectures | 29:13
Bar charts
14:09
Line charts
08:53
Scatterplots
06:11
4 Lectures | 32:21
Marks cards
13:38
Dropping Dimensions and Measures on marks card
09:49
Dropping Dimensions on Line chart
04:10
Adding marks in scatterplot
04:44
8 Lectures | 53:20
Text tables, heat map and highlight tables
08:33
Pie charts
07:34
Area charts
09:06
Creating custom hierarchy
04:04
Tree map
05:00
Dual combination charts
08:16
Creating Bins
06:11
Histogram
04:36
9 Lectures | 01:25:25
Grouping Data
09:18
Filtering data
09:31
Dimension filters
10:55
Measure filters
04:04
Date-Time filters
08:02
Filter options
08:51
Types of filters and order of operation
11:00
Customizing visual filters
09:09
Sorting options
14:35
9 Lectures | 01:09:45
How to make a map chart
07:04
Considerations before making a Map chart
04:46
Marks card for customizing maps
07:26
Customizing maps using map menu
09:33
Layers in a Map
07:56
Visual toolbar on a map
04:35
Custom background images
11:04
Territories in maps
06:12
Data blending for missing geocoding
11:09
7 Lectures | 01:37:45
Calculated fields in Tableau
15:07
Functions in Tableau
02:53
Table calculations theory
07:06
Table calculations in Tableau
09:38
Understanding LOD expressions
27:11
LOD expressions examples
15:49
Analytics pane
20:01
3 Lectures | 30:10
Understanding sets in Tableau
05:34
Creating Sets in Tableau
09:51
Parameters
14:45
3 Lectures | 32:58
Dashboard part -1
17:04
Dashboard part - 2
10:45
Story
05:09
2 Lectures | 09:19
Connecting to SQL data source
05:26
Connecting to cloud storage services
03:53
1 Lectures | 01:49
Introduction
01:49
9 Lectures | 01:37:54
Installing Python and Anaconda
03:04
Opening Jupyter Notebook
09:04
Introduction to Jupyter
13:27
Arithmetic operators in Python: Python Basics
04:28
Strings in Python: Python Basics
19:07
Lists, Tuples and Directories: Python Basics
18:40
Working with Numpy Library of Python
11:52
Working with Pandas Library of Python
09:15
Working with Seaborn Library of Python
08:57
5 Lectures | 30:08
Types of Data
04:04
Types of Statistics
02:45
Describing data Graphically
11:37
Measures of Centers
07:05
Measures of Dispersion
04:37
2 Lectures | 24:45
Introduction to Machine Learning
16:03
Building a Machine Learning Model
08:42
18 Lectures | 02:02:54
Gathering Business Knowledge
02:53
Data Exploration
03:19
The Dataset and the Data Dictionary
06:36
Importing Data in Python
06:04
Univariate analysis and EDD
03:31
EDD in Python
12:11
Outlier Treatment
04:15
Outlier Treatment in Python
14:18
Missing Value Imputation
03:36
Missing Value Imputation in Python
04:57
Seasonality in Data
03:35
Bi-variate analysis and Variable transformation
16:14
Variable transformation and deletion in Python
09:21
Non-usable variables
04:44
Dummy variable creation: Handling qualitative data
04:46
Dummy variable creation in Python
05:45
Correlation Analysis
09:42
Correlation Analysis in Python
07:07
17 Lectures | 02:33:08
The Problem Statement
01:22
Basic Equations and Ordinary Least Squares (OLS) method
07:46
Assessing accuracy of predicted coefficients
14:40
Assessing Model Accuracy: RSE and R squared
07:19
Simple Linear Regression in Python
14:07
Multiple Linear Regression
04:58
The F - statistic
08:22
Interpreting results of Categorical variables
05:04
Multiple Linear Regression in Python
14:13
Test-train split
09:32
Bias Variance trade-off
06:01
Test train split in Python
10:17
Regression models other than OLS
04:18
Subset selection techniques
11:34
Shrinkage methods: Ridge and Lasso
07:14
Ridge regression and Lasso in Python
23:51
Heteroscedasticity
02:30
4 Lectures | 13:07
Three classification models and Data set
05:31
Importing the data into Python
01:36
The problem statements
01:28
Why can't we use Linear Regression?
04:32
9 Lectures | 57:42
Logistic Regression
07:54
Training a Simple Logistic Model in Python
12:25
Result of Simple Logistic Regression
05:11
Logistic with multiple predictors
02:22
Training multiple predictor Logistic model in Python
06:04
Confusion Matrix
03:47
Creating Confusion Matrix in Python
09:56
Evaluating performance of model
07:41
Evaluating model performance in Python
02:22
2 Lectures | 12:09
Linear Discriminant Analysis
09:39
LDA in Python
02:30
5 Lectures | 41:23
Test-Train Split
09:32
Test-Train Split in Python
10:19
K-Nearest Neighbors classifier
08:41
K-Nearest Neighbors in Python: Part 1
05:51
K-Nearest Neighbors in Python: Part 2
07:00
2 Lectures | 10:38
Understanding the results of classification models
06:06
Summary of the three models
04:32
14 Lectures | 01:13:15
Introduction to Decision trees
03:39
Basics of Decision Trees
10:10
Understanding a Regression Tree
10:17
The stopping criteria for controlling tree growth
03:15
Importing the Data set into Python
02:53
Missing value treatment in Python
02:18
Dummy Variable Creation in Python
04:03
Dependent- Independent Data split in Python
03:36
Test-Train split in Python
05:15
Creating Decision tree in Python
03:47
Evaluating model performance in Python
04:10
Plotting decision tree in Python
04:59
Pruning a tree
04:16
Pruning a tree in Python
10:37
5 Lectures | 30:56
Classification tree
06:06
The Data set for Classification problem
01:38
Classification tree in Python : Preprocessing
08:25
Classification tree in Python : Training
13:13
Advantages and Disadvantages of Decision Trees
01:34
2 Lectures | 17:44
Ensemble technique 1 - Bagging
06:39
Ensemble technique 1 - Bagging in Python
11:05
3 Lectures | 22:16
Ensemble technique 2 - Random Forests
03:56
Ensemble technique 2 - Random Forests in Python
06:06
Using Grid Search in Python
12:14
4 Lectures | 27:26
Boosting
07:11
Ensemble technique 3a - Boosting in Python
05:08
Ensemble technique 3b - AdaBoost in Python
04:00
Ensemble technique 3c - XGBoost in Python
11:07
5 Lectures | 35:56
Introduction to Neural Networks and Course flow
04:45
Perceptron
09:44
Activation Functions
07:30
Creating Perceptron model in Python - Part 1
07:38
Creating Perceptron model in Python - Part 2
06:19
6 Lectures | 01:05:23
Basic Terminologies
09:47
Gradient Descent
12:17
Back Propagation Part - 1
10:01
Back Propagation - Part 2
12:15
Some Important Concepts
12:44
Hyperparameter
08:19
15 Lectures | 01:58:41
Keras and Tensorflow
03:04
Installing Tensorflow and Keras
04:01
Dataset for classification
07:19
Normalization and Test-Train split
06:04
Different ways to create ANN using Keras
01:58
Building the Neural Network using Keras
12:15
Compiling and Training the Neural Network model
10:59
Evaluating performance and Predicting using Keras
09:21
Building Neural Network for Regression Problem - Part 1
06:01
Building Neural Network for Regression Problem - Part 2
09:33
Building Neural Network for Regression Problem - Part 3
06:34
Using Functional API for complex architectures
12:40
Saving - Restoring Models and Using Callbacks - Part 1
12:58
Saving - Restoring Models and Using Callbacks - Part 2
06:49
Hyperparameter Tuning
09:05
6 Lectures | 35:31
CNN Introduction
07:43
Stride
02:50
Padding
05:07
Filters and feature map
07:48
Channels
06:31
Pooling Layer
05:32
4 Lectures | 25:18
CNN model in Python - Preprocessing
05:42
CNN model in Python - structure and Compile
06:24
CNN model in Python - Training and results
06:52
Comparison - Pooling vs Without Pooling in Python
06:20
5 Lectures | 28:31
Project - Introduction
07:05
Data for the project
00:02
Project - Data Preprocessing in Python
09:16
Project - Training CNN model in Python
09:01
Project in Python - model results
03:07
2 Lectures | 13:09
Project - Data Augmentation Preprocessing
06:44
Project - Data Augmentation Training and Results
06:25
9 Lectures | 36:59
ILSVRC
04:08
LeNET
01:31
VGG16NET
02:00
GoogLeNet
02:52
Transfer Learning
05:15
Project - Transfer Learning - VGG16 - Part - 1
08:17
Project - Transfer Learning - VGG16 - Part - 2
08:08
Project - Transfer Learning - VGG16 - Part - 3
03:15
The final milestone!
01:33
1 Lectures | 00:57
Bonus Lecture
00:57
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