Become A Data Scientist: Sql, Tableau, Ml & Dl Using Python

4-in-1 Bundle covering the 4 essential topics for a data scientist - SQL, Tableau, Machine & Deep Learning using Python

What you'll learn

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.

Requirements

A PC with internet connection. Installation instructions for all tools used are part of the course.

Description

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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Installation and getting started

Lecture 2 Installing PostgreSQL and pgAdmin in your PC

Lecture 3 This is a milestone!

Lecture 4 If pgAdmin is not opening…

Lecture 5 Course Resources

Section 3: Case Study : Demo

Lecture 6 Case Study Part 1 - Business problems

Lecture 7 Case Study Part 2 - How SQL is Used

Section 4: Fundamental SQL statements

Lecture 8 CREATE

Lecture 9 INSERT

Lecture 10 Import data from File

Lecture 11 SELECT statement

Lecture 12 SELECT DISTINCT

Lecture 13 WHERE

Lecture 14 Logical Operators

Lecture 15 UPDATE

Lecture 16 DELETE

Lecture 17 ALTER Part - 1

Lecture 18 ALTER Part - 2

Section 5: Restore and Back-up

Lecture 19 Restore and Back-up

Lecture 20 Debugging restoration issues

Lecture 21 Creating DB using CSV files

Lecture 22 Debugging summary and Code for CSV files

Section 6: Selection commands: Filtering

Lecture 23 IN

Lecture 24 BETWEEN

Lecture 25 LIKE

Section 7: Selection commands: Ordering

Lecture 26 Side Lecture: Commenting in SQL

Lecture 27 ORDER BY

Lecture 28 LIMIT

Section 8: Alias

Lecture 29 AS

Section 9: Aggregate Commands

Lecture 30 COUNT

Lecture 31 SUM

Lecture 32 AVERAGE

Lecture 33 MIN & MAX

Section 10: Group By Commands

Lecture 34 GROUP BY

Lecture 35 HAVING

Section 11: Conditional Statement

Lecture 36 CASE WHEN

Section 12: JOINS

Lecture 37 Introduction to Joins

Lecture 38 Concepts of Joining and Combining Data

Lecture 39 Preparing the data

Lecture 40 Inner Join

Lecture 41 Left Join

Lecture 42 Right Join

Lecture 43 Full Outer Join

Lecture 44 Cross Join

Lecture 45 Intersect and Intersect ALL

Lecture 46 Except

Lecture 47 Union

Section 13: Subqueries

Lecture 48 Subquery in WHERE clause

Lecture 49 Subquery in FROM clause

Lecture 50 Subquery in SELECT clause

Section 14: Views and Indexes

Lecture 51 VIEWS

Lecture 52 INDEX

Section 15: String Functions

Lecture 53 LENGTH

Lecture 54 UPPER LOWER

Lecture 55 REPLACE

Lecture 56 TRIM, LTRIM, RTRIM

Lecture 57 CONCATENATION

Lecture 58 SUBSTRING

Lecture 59 LIST AGGREGATION

Section 16: Mathematical Functions

Lecture 60 CEIL & FLOOR

Lecture 61 RANDOM

Lecture 62 SETSEED

Lecture 63 ROUND

Lecture 64 POWER

Section 17: Date-Time Functions

Lecture 65 CURRENT DATE & TIME

Lecture 66 AGE

Lecture 67 EXTRACT

Section 18: PATTERN (STRING) MATCHING

Lecture 68 PATTERN MATCHING BASICS

Lecture 69 ADVANCE PATTERN MATCHING - Part 1

Lecture 70 ADVANCE PATTERN MATCHING - Part 2

Section 19: Window Functions

Lecture 71 Introduction to Window functions

Lecture 72 Introduction to Row number

Lecture 73 Implementing Row number in SQL

Lecture 74 RANK and DENSERANK

Lecture 75 NTILE function

Lecture 76 AVERAGE function

Lecture 77 COUNT

Lecture 78 SUM TOTAL

Lecture 79 RUNNING TOTAL

Lecture 80 LAG and LEAD

Section 20: COALESCE function

Lecture 81 COALESCE function

Section 21: Data Type conversion functions

Lecture 82 Converting Numbers/ Date to String

Lecture 83 Converting String to Numbers/ Date

Section 22: User Access Control Functions

Lecture 84 User Access Control - Part 1

Lecture 85 User Access Control - Part 2

Section 23: Nail that Interview!

Lecture 86 Tablespace

Lecture 87 PRIMARY KEY & FOREIGN KEY

Lecture 88 ACID compliance

Lecture 89 Truncate

Section 24: TABLEAU

Lecture 90 Why Tableau

Lecture 91 Tableau Products

Section 25: Installing and getting started

Lecture 92 Installing Tableau desktop and Public

Lecture 93 About the data

Lecture 94 Connecting to data

Lecture 95 Live vs Extract

Section 26: Combining data to create Data model

Lecture 96 Combining data from multiple tables

Lecture 97 Relationships in Tableau

Lecture 98 Joins in Tableau

Lecture 99 Types of Joins in Tableau

Lecture 100 Union in Tableau

Lecture 101 Physical Logical layer and Data models

Lecture 102 The visualization screen - Sheet

Section 27: Data categorization in Tableau

Lecture 103 Types of Data - Dimensions and Measures

Lecture 104 Types of Data - Discreet and Continuous

Lecture 105 Changing Data type in Tableau

Section 28: Most used charts

Lecture 106 Bar charts

Lecture 107 Line charts

Lecture 108 Scatterplots

Section 29: Customizing charts using Marks shelf

Lecture 109 Marks cards

Lecture 110 Dropping Dimensions and Measures on marks card

Lecture 111 Dropping Dimensions on Line chart

Lecture 112 Adding marks in scatterplot

Section 30: Other important charts

Lecture 113 Text tables, heat map and highlight tables

Lecture 114 Pie charts

Lecture 115 Area charts

Lecture 116 Creating custom hierarchy

Lecture 117 Tree map

Lecture 118 Dual combination charts

Lecture 119 Creating Bins

Lecture 120 Histogram

Section 31: Grouping and Filtering data

Lecture 121 Grouping Data

Lecture 122 Filtering data

Lecture 123 Dimension filters

Lecture 124 Measure filters

Lecture 125 Date-Time filters

Lecture 126 Filter options

Lecture 127 Types of filters and order of operation

Lecture 128 Customizing visual filters

Lecture 129 Sorting options

Section 32: Map charts in Tableau

Lecture 130 How to make a map chart

Lecture 131 Considerations before making a Map chart

Lecture 132 Marks card for customizing maps

Lecture 133 Customizing maps using map menu

Lecture 134 Layers in a Map

Lecture 135 Visual toolbar on a map

Lecture 136 Custom background images

Lecture 137 Territories in maps

Lecture 138 Data blending for missing geocoding

Section 33: Calculation and Analytics

Lecture 139 Calculated fields in Tableau

Lecture 140 Functions in Tableau

Lecture 141 Table calculations theory

Lecture 142 Table calculations in Tableau

Lecture 143 Understanding LOD expressions

Lecture 144 LOD expressions examples

Lecture 145 Analytics pane

Section 34: Sets and Parameters

Lecture 146 Understanding sets in Tableau

Lecture 147 Creating Sets in Tableau

Lecture 148 Parameters

Section 35: Dashboard and Story

Lecture 149 Dashboard part -1

Lecture 150 Dashboard part - 2

Lecture 151 Story

Section 36: Appendix

Lecture 152 Connecting to SQL data source

Lecture 153 Connecting to cloud storage services

Section 37: Machine Learning with Python

Lecture 154 Introduction

Section 38: Setting up Python and Jupyter notebook

Lecture 155 Installing Python and Anaconda

Lecture 156 Opening Jupyter Notebook

Lecture 157 Introduction to Jupyter

Lecture 158 Arithmetic operators in Python: Python Basics

Lecture 159 Strings in Python: Python Basics

Lecture 160 Lists, Tuples and Directories: Python Basics

Lecture 161 Working with Numpy Library of Python

Lecture 162 Working with Pandas Library of Python

Lecture 163 Working with Seaborn Library of Python

Section 39: Basics of statistics

Lecture 164 Types of Data

Lecture 165 Types of Statistics

Lecture 166 Describing data Graphically

Lecture 167 Measures of Centers

Lecture 168 Measures of Dispersion

Section 40: Introduction to Machine Learning

Lecture 169 Introduction to Machine Learning

Lecture 170 Building a Machine Learning Model

Section 41: Data Preprocessing

Lecture 171 Gathering Business Knowledge

Lecture 172 Data Exploration

Lecture 173 The Dataset and the Data Dictionary

Lecture 174 Importing Data in Python

Lecture 175 Univariate analysis and EDD

Lecture 176 EDD in Python

Lecture 177 Outlier Treatment

Lecture 178 Outlier Treatment in Python

Lecture 179 Missing Value Imputation

Lecture 180 Missing Value Imputation in Python

Lecture 181 Seasonality in Data

Lecture 182 Bi-variate analysis and Variable transformation

Lecture 183 Variable transformation and deletion in Python

Lecture 184 Non-usable variables

Lecture 185 Dummy variable creation: Handling qualitative data

Lecture 186 Dummy variable creation in Python

Lecture 187 Correlation Analysis

Lecture 188 Correlation Analysis in Python

Section 42: Linear Regression

Lecture 189 The Problem Statement

Lecture 190 Basic Equations and Ordinary Least Squares (OLS) method

Lecture 191 Assessing accuracy of predicted coefficients

Lecture 192 Assessing Model Accuracy: RSE and R squared

Lecture 193 Simple Linear Regression in Python

Lecture 194 Multiple Linear Regression

Lecture 195 The F - statistic

Lecture 196 Interpreting results of Categorical variables

Lecture 197 Multiple Linear Regression in Python

Lecture 198 Test-train split

Lecture 199 Bias Variance trade-off

Lecture 200 Test train split in Python

Lecture 201 Regression models other than OLS

Lecture 202 Subset selection techniques

Lecture 203 Shrinkage methods: Ridge and Lasso

Lecture 204 Ridge regression and Lasso in Python

Lecture 205 Heteroscedasticity

Section 43: Introduction to the classification Models

Lecture 206 Three classification models and Data set

Lecture 207 Importing the data into Python

Lecture 208 The problem statements

Lecture 209 Why can't we use Linear Regression?

Section 44: Logistic Regression

Lecture 210 Logistic Regression

Lecture 211 Training a Simple Logistic Model in Python

Lecture 212 Result of Simple Logistic Regression

Lecture 213 Logistic with multiple predictors

Lecture 214 Training multiple predictor Logistic model in Python

Lecture 215 Confusion Matrix

Lecture 216 Creating Confusion Matrix in Python

Lecture 217 Evaluating performance of model

Lecture 218 Evaluating model performance in Python

Section 45: Linear Discriminant Analysis (LDA)

Lecture 219 Linear Discriminant Analysis

Lecture 220 LDA in Python

Section 46: K Nearest neighbors classifier

Lecture 221 Test-Train Split

Lecture 222 Test-Train Split in Python

Lecture 223 K-Nearest Neighbors classifier

Lecture 224 K-Nearest Neighbors in Python: Part 1

Lecture 225 K-Nearest Neighbors in Python: Part 2

Section 47: Comparing results from 3 models

Lecture 226 Understanding the results of classification models

Lecture 227 Summary of the three models

Section 48: Simple Decision Trees

Lecture 228 Introduction to Decision trees

Lecture 229 Basics of Decision Trees

Lecture 230 Understanding a Regression Tree

Lecture 231 The stopping criteria for controlling tree growth

Lecture 232 Importing the Data set into Python

Lecture 233 Missing value treatment in Python

Lecture 234 Dummy Variable Creation in Python

Lecture 235 Dependent- Independent Data split in Python

Lecture 236 Test-Train split in Python

Lecture 237 Creating Decision tree in Python

Lecture 238 Evaluating model performance in Python

Lecture 239 Plotting decision tree in Python

Lecture 240 Pruning a tree

Lecture 241 Pruning a tree in Python

Section 49: Simple Classification Trees

Lecture 242 Classification tree

Lecture 243 The Data set for Classification problem

Lecture 244 Classification tree in Python : Preprocessing

Lecture 245 Classification tree in Python : Training

Lecture 246 Advantages and Disadvantages of Decision Trees

Section 50: Ensemble technique 1 - Bagging

Lecture 247 Ensemble technique 1 - Bagging

Lecture 248 Ensemble technique 1 - Bagging in Python

Section 51: Ensemble technique 2 - Random Forests

Lecture 249 Ensemble technique 2 - Random Forests

Lecture 250 Ensemble technique 2 - Random Forests in Python

Lecture 251 Using Grid Search in Python

Section 52: Ensemble technique 3 - Boosting

Lecture 252 Boosting

Lecture 253 Ensemble technique 3a - Boosting in Python

Lecture 254 Ensemble technique 3b - AdaBoost in Python

Lecture 255 Ensemble technique 3c - XGBoost in Python

Section 53: Introduction - Deep Learning

Lecture 256 Introduction to Neural Networks and Course flow

Lecture 257 Perceptron

Lecture 258 Activation Functions

Lecture 259 Creating Perceptron model in Python - Part 1

Lecture 260 Creating Perceptron model in Python - Part 2

Section 54: Neural Networks - Stacking cells to create network

Lecture 261 Basic Terminologies

Lecture 262 Gradient Descent

Lecture 263 Back Propagation Part - 1

Lecture 264 Back Propagation - Part 2

Lecture 265 Some Important Concepts

Lecture 266 Hyperparameter

Section 55: ANN in Python

Lecture 267 Keras and Tensorflow

Lecture 268 Installing Tensorflow and Keras

Lecture 269 Dataset for classification

Lecture 270 Normalization and Test-Train split

Lecture 271 Different ways to create ANN using Keras

Lecture 272 Building the Neural Network using Keras

Lecture 273 Compiling and Training the Neural Network model

Lecture 274 Evaluating performance and Predicting using Keras

Lecture 275 Building Neural Network for Regression Problem - Part 1

Lecture 276 Building Neural Network for Regression Problem - Part 2

Lecture 277 Building Neural Network for Regression Problem - Part 3

Lecture 278 Using Functional API for complex architectures

Lecture 279 Saving - Restoring Models and Using Callbacks - Part 1

Lecture 280 Saving - Restoring Models and Using Callbacks - Part 2

Lecture 281 Hyperparameter Tuning

Section 56: CNN Basics

Lecture 282 CNN Introduction

Lecture 283 Stride

Lecture 284 Padding

Lecture 285 Filters and feature map

Lecture 286 Channels

Lecture 287 Pooling Layer

Section 57: Creating CNN model in Python

Lecture 288 CNN model in Python - Preprocessing

Lecture 289 CNN model in Python - structure and Compile

Lecture 290 CNN model in Python - Training and results

Lecture 291 Comparison - Pooling vs Without Pooling in Python

Section 58: Project: Creating CNN model from scratch in Python

Lecture 292 Project - Introduction

Lecture 293 Data for the project

Lecture 294 Project - Data Preprocessing in Python

Lecture 295 Project - Training CNN model in Python

Lecture 296 Project in Python - model results

Section 59: Project : Data Augmentation for avoiding overfitting

Lecture 297 Project - Data Augmentation Preprocessing

Lecture 298 Project - Data Augmentation Training and Results

Section 60: Transfer Learning : Basics

Lecture 299 ILSVRC

Lecture 300 LeNET

Lecture 301 VGG16NET

Lecture 302 GoogLeNet

Lecture 303 Transfer Learning

Lecture 304 Project - Transfer Learning - VGG16 - Part - 1

Lecture 305 Project - Transfer Learning - VGG16 - Part - 2

Lecture 306 Project - Transfer Learning - VGG16 - Part - 3

Lecture 307 The final milestone!

Section 61: Congratulations & about your certificate

Lecture 308 Bonus Lecture

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 science,Small business owners who want to use data to drive better decision-making in their companies