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 Python
- Build strong foundations in data analysis, data visualization, and data modeling
- Acquire hands-on experience in working with real-world datasets
- Gain a deep understanding of the underlying concepts of Machine and Deep Learning
- Learn to build and train your own predictive models using Python
Data 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 course
- Part 1 – SQL for data science
In 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 Tableau
In 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 Python
In 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 Python
In 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.
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Throughout the course, you will work on several activities such as:
- Building an SQL database and retrieving relevant data from it
- Creating interactive dashboards using Tableau
- Implementing various Machine Learning algorithms
- Building a Deep Learning model using Keras and TensorFlow
This 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 – Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Our Promise
Teaching 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.
Introduction
Introduction
Installation and getting started
Installing PostgreSQL and pgAdmin in your PC
This is a milestone!
If pgAdmin is not opening…
Course Resources
Case Study : Demo
Case Study Part 1 – Business problems
Case Study Part 2 – How SQL is Used
Fundamental SQL statements
CREATE
INSERT
Import data from File
SELECT statement
SELECT DISTINCT
WHERE
Logical Operators
UPDATE
DELETE
ALTER Part – 1
ALTER Part – 2
Restore and Back-up
Restore and Back-up
Debugging restoration issues
Creating DB using CSV files
Debugging summary and Code for CSV files
Selection commands: Filtering
IN
BETWEEN
LIKE
Selection commands: Ordering
Side Lecture: Commenting in SQL
ORDER BY
LIMIT
Aggregate Commands
COUNT
SUM
AVERAGE
MIN & MAX
Group By Commands
GROUP BY
HAVING
Conditional Statement
CASE WHEN
JOINS
Introduction to Joins
Concepts of Joining and Combining Data
Preparing the data
Inner Join
Left Join
Right Join
Full Outer Join
Cross Join
Intersect and Intersect ALL
Except
Union
Subqueries
Subquery in WHERE clause
Subquery in FROM clause
Subquery in SELECT clause
Views and Indexes
VIEWS
INDEX
String Functions
LENGTH
UPPER LOWER
REPLACE
TRIM, LTRIM, RTRIM
CONCATENATION
SUBSTRING
LIST AGGREGATION
Mathematical Functions
CEIL & FLOOR
RANDOM
SETSEED
ROUND
POWER
Date-Time Functions
CURRENT DATE & TIME
AGE
EXTRACT
PATTERN (STRING) MATCHING
PATTERN MATCHING BASICS
ADVANCE PATTERN MATCHING – Part 1
ADVANCE PATTERN MATCHING – Part 2
Window Functions
Introduction to Window functions
Introduction to Row number
Implementing Row number in SQL
RANK and DENSERANK
NTILE function
AVERAGE function
COUNT
SUM TOTAL
RUNNING TOTAL
LAG and LEAD
COALESCE function
COALESCE function
Data Type conversion functions
Converting Numbers/ Date to String
Converting String to Numbers/ Date
User Access Control Functions
User Access Control – Part 1
User Access Control – Part 2
Nail that Interview!
Tablespace
PRIMARY KEY & FOREIGN KEY
ACID compliance
Truncate
TABLEAU
Why Tableau
Tableau Products
Installing and getting started
Installing Tableau desktop and Public
About the data
Connecting to data
Live vs Extract
Combining data to create Data model
Combining data from multiple tables
Relationships in Tableau
Joins in Tableau
Types of Joins in Tableau
Union in Tableau
Physical Logical layer and Data models
The visualization screen – Sheet
Data categorization in Tableau
Types of Data – Dimensions and Measures
Types of Data – Discreet and Continuous
Changing Data type in Tableau
Most used charts
Bar charts
Line charts
Scatterplots
Customizing charts using Marks shelf
Marks cards
Dropping Dimensions and Measures on marks card
Dropping Dimensions on Line chart
Adding marks in scatterplot
Other important charts
Text tables, heat map and highlight tables
Pie charts
Area charts
Creating custom hierarchy
Tree map
Dual combination charts
Creating Bins
Histogram
Grouping and Filtering data
Grouping Data
Filtering data
Dimension filters
Measure filters
Date-Time filters
Filter options
Types of filters and order of operation
Customizing visual filters
Sorting options
Map charts in Tableau
How to make a map chart
Considerations before making a Map chart
Marks card for customizing maps
Customizing maps using map menu
Layers in a Map
Visual toolbar on a map
Custom background images
Territories in maps
Data blending for missing geocoding
Calculation and Analytics
Calculated fields in Tableau
Functions in Tableau
Table calculations theory
Table calculations in Tableau
Understanding LOD expressions
LOD expressions examples
Analytics pane
Sets and Parameters
Understanding sets in Tableau
Creating Sets in Tableau
Parameters
Dashboard and Story
Dashboard part -1
Dashboard part – 2
Story
Appendix
Connecting to SQL data source
Connecting to cloud storage services
Machine Learning with Python
Introduction
Setting up Python and Jupyter notebook
Installing Python and Anaconda
Opening Jupyter Notebook
Introduction to Jupyter
Arithmetic operators in Python: Python Basics
Strings in Python: Python Basics
Lists, Tuples and Directories: Python Basics
Working with Numpy Library of Python
Working with Pandas Library of Python
Working with Seaborn Library of Python
Basics of statistics
Types of Data
Types of Statistics
Describing data Graphically
Measures of Centers
Measures of Dispersion
Introduction to Machine Learning
Introduction to Machine Learning
Building a Machine Learning Model
Data Preprocessing
Gathering Business Knowledge
Data Exploration
The Dataset and the Data Dictionary
Importing Data in Python
Univariate analysis and EDD
EDD in Python
Outlier Treatment
Outlier Treatment in Python
Missing Value Imputation
Missing Value Imputation in Python
Seasonality in Data
Bi-variate analysis and Variable transformation
Variable transformation and deletion in Python
Non-usable variables
Dummy variable creation: Handling qualitative data
Dummy variable creation in Python
Correlation Analysis
Correlation Analysis in Python
Linear Regression
The Problem Statement
Basic Equations and Ordinary Least Squares (OLS) method
Assessing accuracy of predicted coefficients
Assessing Model Accuracy: RSE and R squared
Simple Linear Regression in Python
Multiple Linear Regression
The F – statistic
Interpreting results of Categorical variables
Multiple Linear Regression in Python
Test-train split
Bias Variance trade-off
Test train split in Python
Regression models other than OLS
Subset selection techniques
Shrinkage methods: Ridge and Lasso
Ridge regression and Lasso in Python
Heteroscedasticity
Introduction to the classification Models
Three classification models and Data set
Importing the data into Python
The problem statements
Why can’t we use Linear Regression?
Logistic Regression
Logistic Regression
Training a Simple Logistic Model in Python
Result of Simple Logistic Regression
Logistic with multiple predictors
Training multiple predictor Logistic model in Python
Confusion Matrix
Creating Confusion Matrix in Python
Evaluating performance of model
Evaluating model performance in Python
Linear Discriminant Analysis (LDA)
Linear Discriminant Analysis
LDA in Python
K Nearest neighbors classifier
Test-Train Split
Test-Train Split in Python
K-Nearest Neighbors classifier
K-Nearest Neighbors in Python: Part 1
K-Nearest Neighbors in Python: Part 2
Comparing results from 3 models
Understanding the results of classification models
Summary of the three models
Simple Decision Trees
Introduction to Decision trees
Basics of Decision Trees
Understanding a Regression Tree
The stopping criteria for controlling tree growth
Importing the Data set into Python
Missing value treatment in Python
Dummy Variable Creation in Python
Dependent- Independent Data split in Python
Test-Train split in Python
Creating Decision tree in Python
Evaluating model performance in Python
Plotting decision tree in Python
Pruning a tree
Pruning a tree in Python
Simple Classification Trees
Classification tree
The Data set for Classification problem
Classification tree in Python : Preprocessing
Classification tree in Python : Training
Advantages and Disadvantages of Decision Trees
Ensemble technique 1 – Bagging
Ensemble technique 1 – Bagging
Ensemble technique 1 – Bagging in Python
Ensemble technique 2 – Random Forests
Ensemble technique 2 – Random Forests
Ensemble technique 2 – Random Forests in Python
Using Grid Search in Python
Ensemble technique 3 – Boosting
Boosting
Ensemble technique 3a – Boosting in Python
Ensemble technique 3b – AdaBoost in Python
Ensemble technique 3c – XGBoost in Python
Introduction – Deep Learning
Introduction to Neural Networks and Course flow
Perceptron
Activation Functions
Creating Perceptron model in Python – Part 1
Creating Perceptron model in Python – Part 2
Neural Networks – Stacking cells to create network
Basic Terminologies
Gradient Descent
Back Propagation Part – 1
Back Propagation – Part 2
Some Important Concepts
Hyperparameter
ANN in Python
Keras and Tensorflow
Installing Tensorflow and Keras
Dataset for classification
Normalization and Test-Train split
Different ways to create ANN using Keras
Building the Neural Network using Keras
Compiling and Training the Neural Network model
Evaluating performance and Predicting using Keras
Building Neural Network for Regression Problem – Part 1
Building Neural Network for Regression Problem – Part 2
Building Neural Network for Regression Problem – Part 3
Using Functional API for complex architectures
Saving – Restoring Models and Using Callbacks – Part 1
Saving – Restoring Models and Using Callbacks – Part 2
Hyperparameter Tuning
CNN Basics
CNN Introduction
Stride
Padding
Filters and feature map
Channels
Pooling Layer
Creating CNN model in Python
CNN model in Python – Preprocessing
CNN model in Python – structure and Compile
CNN model in Python – Training and results
Comparison – Pooling vs Without Pooling in Python
Project: Creating CNN model from scratch in Python
Project – Introduction
Data for the project
Project – Data Preprocessing in Python
Project – Training CNN model in Python
Project in Python – model results
Project : Data Augmentation for avoiding overfitting
Project – Data Augmentation Preprocessing
Project – Data Augmentation Training and Results
Transfer Learning : Basics
ILSVRC
LeNET
VGG16NET
GoogLeNet
Transfer Learning
Project – Transfer Learning – VGG16 – Part – 1
Project – Transfer Learning – VGG16 – Part – 2
Project – Transfer Learning – VGG16 – Part – 3
The final milestone!
Congratulations & about your certificate
Bonus Lecture
