Become a Data Scientist: SQL, Tableau, ML & DL [4-in-1]

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Start-Tech Academy

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1. Introduction

1. Introduction.mp4

04:07

2. Installation and getting started

1. Installing PostgreSQL and pgAdmin in your PC.mp4

10:44

2. This is a milestone!.mp4

03:31

3. If pgAdmin is not opening .html

4. Course Resources.html

3. Case Study Demo

1. Case Study Part 1 - Business problems.mp4

04:21

2. Case Study Part 2 - How SQL is Used.mp4

06:17

4. Fundamental SQL statements

1. CREATE.mp4

11:40

2. INSERT.mp4

09:07

3. Import data from File.mp4

04:59

4. SELECT statement.mp4

03:40

5. SELECT DISTINCT.mp4

06:05

6. WHERE.mp4

04:02

7. Logical Operators.mp4

06:03

8. UPDATE.mp4

05:24

9. DELETE.mp4

04:11

10. ALTER Part - 1.mp4

06:49

11. ALTER Part - 2.mp4

10:31

5. Restore and Back-up

1. Restore and Back-up.mp4

07:37

2. Debugging restoration issues.mp4

08:26

3. Creating DB using CSV files.mp4

05:40

4. Debugging summary and Code for CSV files.html

6. Selection commands Filtering

1. IN.mp4

04:18

2. BETWEEN.mp4

05:40

3. LIKE.mp4

08:52

7. Selection commands Ordering

1. Side Lecture Commenting in SQL.mp4

01:21

2. ORDER BY.mp4

07:42

3. LIMIT.mp4

03:38

8. Alias

1. AS.mp4

03:33

9. Aggregate Commands

1. COUNT.mp4

05:07

2. SUM.mp4

03:24

3. AVERAGE.mp4

02:53

4. MIN & MAX.mp4

04:18

10. Group By Commands

1. GROUP BY.mp4

11:42

2. HAVING.mp4

05:04

11. Conditional Statement

1. CASE WHEN.mp4

05:17

12. JOINS

1. Introduction to Joins.mp4

02:54

2. Concepts of Joining and Combining Data.mp4

11:58

3. Preparing the data.mp4

02:00

4. Inner Join.mp4

08:04

5. Left Join.mp4

07:30

6. Right Join.mp4

06:27

7. Full Outer Join.mp4

04:59

8. Cross Join.mp4

04:21

9. Intersect and Intersect ALL.mp4

07:06

10. Except.mp4

02:53

11. Union.mp4

03:09

13. Subqueries

1. Subquery in WHERE clause.mp4

04:55

2. Subquery in FROM clause.mp4

05:23

3. Subquery in SELECT clause.mp4

04:02

14. Views and Indexes

1. VIEWS.mp4

07:14

2. INDEX.mp4

06:25

15. String Functions

1. LENGTH.mp4

03:22

2. UPPER LOWER.mp4

02:10

3. REPLACE.mp4

04:13

4. TRIM, LTRIM, RTRIM.mp4

06:56

5. CONCATENATION.mp4

02:56

6. SUBSTRING.mp4

06:01

7. LIST AGGREGATION.mp4

04:54

16. Mathematical Functions

1. CEIL & FLOOR.mp4

03:20

2. RANDOM.mp4

05:04

3. SETSEED.mp4

04:11

4. ROUND.mp4

02:27

5. POWER.mp4

02:18

17. Date-Time Functions

1. CURRENT DATE & TIME.mp4

04:25

2. AGE.mp4

03:50

3. EXTRACT.mp4

08:16

18. PATTERN (STRING) MATCHING

1. PATTERN MATCHING BASICS.mp4

07:33

2. ADVANCE PATTERN MATCHING - Part 1.mp4

08:29

3. ADVANCE PATTERN MATCHING - Part 2.mp4

06:49

19. Window Functions

1. Introduction to Window functions.mp4

09:57

2. Introduction to Row number.mp4

06:04

3. Implementing Row number in SQL.mp4

19:19

4. RANK and DENSERANK.mp4

07:19

5. NTILE function.mp4

07:20

6. AVERAGE function.mp4

08:22

7. COUNT.mp4

03:55

8. SUM TOTAL.mp4

11:14

9. RUNNING TOTAL.mp4

06:58

10. LAG and LEAD.mp4

08:17

20. COALESCE function

1. COALESCE function.mp4

05:57

21. Data Type conversion functions

1. Converting Numbers Date to String.mp4

10:46

2. Converting String to Numbers Date.mp4

05:49

22. User Access Control Functions

1. User Access Control - Part 1.mp4

07:50

2. User Access Control - Part 2.mp4

05:22

23. Nail that Interview!

1. Tablespace.mp4

05:37

2. PRIMARY KEY & FOREIGN KEY.mp4

05:02

3. ACID compliance.mp4

05:32

4. Truncate.mp4

03:54

24. TABLEAU

1. Why Tableau.mp4

04:41

2. Tableau Products.mp4

09:21

25. Installing and getting started

1. Installing Tableau desktop and Public.mp4

05:02

2. About the data.mp4

09:59

3. Connecting to data.mp4

12:26

4. Live vs Extract.mp4

04:24

26. Combining data to create Data model

1. Combining data from multiple tables.mp4

04:27

2. Relationships in Tableau.mp4

13:56

3. Joins in Tableau.mp4

06:43

4. Types of Joins in Tableau.mp4

06:12

5. Union in Tableau.mp4

07:55

6. Physical Logical layer and Data models.mp4

06:30

7. The visualization screen - Sheet.mp4

09:13

27. Data categorization in Tableau

1. Types of Data - Dimensions and Measures.mp4

06:49

2. Types of Data - Discreet and Continuous.mp4

06:09

3. Changing Data type in Tableau.mp4

09:25

28. Most used charts

1. Bar charts.mp4

14:09

2. Line charts.mp4

08:53

3. Scatterplots.mp4

06:11

29. Customizing charts using Marks shelf

1. Marks cards.mp4

13:38

2. Dropping Dimensions and Measures on marks card.mp4

09:49

3. Dropping Dimensions on Line chart.mp4

04:10

4. Adding marks in scatterplot.mp4

04:44

30. Other important charts

1. Text tables, heat map and highlight tables.mp4

08:33

2. Pie charts.mp4

07:34

3. Area charts.mp4

09:06

4. Creating custom hierarchy.mp4

04:04

5. Tree map.mp4

05:00

6. Dual combination charts.mp4

08:16

7. Creating Bins.mp4

06:11

8. Histogram.mp4

04:36

31. Grouping and Filtering data

1. Grouping Data.mp4

09:18

2. Filtering data.mp4

09:31

3. Dimension filters.mp4

10:55

4. Measure filters.mp4

04:04

5. Date-Time filters.mp4

08:02

6. Filter options.mp4

08:51

7. Types of filters and order of operation.mp4

11:00

8. Customizing visual filters.mp4

09:09

9. Sorting options.mp4

14:35

32. Map charts in Tableau

1. How to make a map chart.mp4

07:04

2. Considerations before making a Map chart.mp4

04:46

3. Marks card for customizing maps.mp4

07:26

4. Customizing maps using map menu.mp4

09:33

5. Layers in a Map.mp4

07:56

6. Visual toolbar on a map.mp4

04:35

7. Custom background images.mp4

11:04

8. Territories in maps.mp4

06:12

9. Data blending for missing geocoding.mp4

11:09

33. Calculation and Analytics

1. Calculated fields in Tableau.mp4

15:07

2. Functions in Tableau.mp4

02:53

3. Table calculations theory.mp4

07:06

4. Table calculations in Tableau.mp4

09:38

5. Understanding LOD expressions.mp4

27:11

6. LOD expressions examples.mp4

15:49

7. Analytics pane.mp4

20:01

34. Sets and Parameters

1. Understanding sets in Tableau.mp4

05:34

2. Creating Sets in Tableau.mp4

09:51

3. Parameters.mp4

14:45

35. Dashboard and Story

1. Dashboard part -1.mp4

17:04

2. Dashboard part - 2.mp4

10:45

3. Story.mp4

05:09

36. Appendix

1. Connecting to SQL data source.mp4

05:26

2. Connecting to cloud storage services.mp4

03:53

37. Machine Learning with Python

1. Introduction.mp4

01:49

38. Setting up Python and Jupyter notebook

1. Installing Python and Anaconda.mp4

03:04

2. Opening Jupyter Notebook.mp4

09:04

3. Introduction to Jupyter.mp4

13:27

4. Arithmetic operators in Python Python Basics.mp4

04:28

5. Strings in Python Python Basics.mp4

19:07

6. Lists, Tuples and Directories Python Basics.mp4

18:40

7. Working with Numpy Library of Python.mp4

11:52

8. Working with Pandas Library of Python.mp4

09:15

9. Working with Seaborn Library of Python.mp4

08:57

39. Basics of statistics

1. Types of Data.mp4

04:04

2. Types of Statistics.mp4

02:45

3. Describing data Graphically.mp4

11:37

4. Measures of Centers.mp4

07:05

5. Measures of Dispersion.mp4

04:37

40. Introduction to Machine Learning

1. Introduction to Machine Learning.mp4

16:03

2. Building a Machine Learning Model.mp4

08:42

41. Data Preprocessing

1. Gathering Business Knowledge.mp4

02:53

2. Data Exploration.mp4

03:19

3. The Dataset and the Data Dictionary.mp4

06:36

4. Importing Data in Python.mp4

06:04

5. Univariate analysis and EDD.mp4

03:31

6. EDD in Python.mp4

12:11

7. Outlier Treatment.mp4

04:15

8. Outlier Treatment in Python.mp4

14:18

9. Missing Value Imputation.mp4

03:36

10. Missing Value Imputation in Python.mp4

04:57

11. Seasonality in Data.mp4

03:35

12. Bi-variate analysis and Variable transformation.mp4

16:14

13. Variable transformation and deletion in Python.mp4

09:21

14. Non-usable variables.mp4

04:44

15. Dummy variable creation Handling qualitative data.mp4

04:46

16. Dummy variable creation in Python.mp4

05:45

17. Correlation Analysis.mp4

09:42

18. Correlation Analysis in Python.mp4

07:07

42. Linear Regression

1. The Problem Statement.mp4

01:22

2. Basic Equations and Ordinary Least Squares (OLS) method.mp4

07:46

3. Assessing accuracy of predicted coefficients.mp4

14:40

4. Assessing Model Accuracy RSE and R squared.mp4

07:19

5. Simple Linear Regression in Python.mp4

14:07

6. Multiple Linear Regression.mp4

04:58

7. The F - statistic.mp4

08:22

8. Interpreting results of Categorical variables.mp4

05:04

9. Multiple Linear Regression in Python.mp4

14:13

10. Test-train split.mp4

09:32

11. Bias Variance trade-off.mp4

06:01

12. Test train split in Python.mp4

10:17

13. Regression models other than OLS.mp4

04:18

14. Subset selection techniques.mp4

11:34

15. Shrinkage methods Ridge and Lasso.mp4

07:14

16. Ridge regression and Lasso in Python.mp4

23:51

17. Heteroscedasticity.mp4

02:30

43. Introduction to the classification Models

1. Three classification models and Data set.mp4

05:31

2. Importing the data into Python.mp4

01:36

3. The problem statements.mp4

01:28

4. Why cant we use Linear Regression.mp4

04:32

44. Logistic Regression

1. Logistic Regression.mp4

07:54

2. Training a Simple Logistic Model in Python.mp4

12:25

3. Result of Simple Logistic Regression.mp4

05:11

4. Logistic with multiple predictors.mp4

02:22

5. Training multiple predictor Logistic model in Python.mp4

06:04

6. Confusion Matrix.mp4

03:47

7. Creating Confusion Matrix in Python.mp4

09:56

8. Evaluating performance of model.mp4

07:41

9. Evaluating model performance in Python.mp4

02:22

45. Linear Discriminant Analysis (LDA)

1. Linear Discriminant Analysis.mp4

09:39

2. LDA in Python.mp4

02:30

46. K Nearest neighbors classifier

1. Test-Train Split.mp4

09:32

2. Test-Train Split in Python.mp4

10:19

3. K-Nearest Neighbors classifier.mp4

08:41

4. K-Nearest Neighbors in Python Part 1.mp4

05:51

5. K-Nearest Neighbors in Python Part 2.mp4

07:00

47. Comparing results from 3 models

1. Understanding the results of classification models.mp4

06:06

2. Summary of the three models.mp4

04:32

48. Simple Decision Trees

1. Introduction to Decision trees.mp4

03:39

2. Basics of Decision Trees.mp4

10:10

3. Understanding a Regression Tree.mp4

10:17

4. The stopping criteria for controlling tree growth.mp4

03:15

5. Importing the Data set into Python.mp4

02:53

6. Missing value treatment in Python.mp4

02:18

7. Dummy Variable Creation in Python.mp4

04:03

8. Dependent- Independent Data split in Python.mp4

03:36

9. Test-Train split in Python.mp4

05:15

10. Creating Decision tree in Python.mp4

03:47

11. Evaluating model performance in Python.mp4

04:10

12. Plotting decision tree in Python.mp4

04:59

13. Pruning a tree.mp4

04:16

14. Pruning a tree in Python.mp4

10:37

49. Simple Classification Trees

1. Classification tree.mp4

06:06

2. The Data set for Classification problem.mp4

01:38

3. Classification tree in Python Preprocessing.mp4

08:25

4. Classification tree in Python Training.mp4

13:13

5. Advantages and Disadvantages of Decision Trees.mp4

01:34

50. Ensemble technique 1 - Bagging

1. Ensemble technique 1 - Bagging.mp4

06:39

2. Ensemble technique 1 - Bagging in Python.mp4

11:05

51. Ensemble technique 2 - Random Forests

1. Ensemble technique 2 - Random Forests.mp4

03:56

2. Ensemble technique 2 - Random Forests in Python.mp4

06:06

3. Using Grid Search in Python.mp4

12:14

52. Ensemble technique 3 - Boosting

1. Boosting.mp4

07:11

2. Ensemble technique 3a - Boosting in Python.mp4

05:08

3. Ensemble technique 3b - AdaBoost in Python.mp4

04:00

4. Ensemble technique 3c - XGBoost in Python.mp4

11:07

53. Introduction - Deep Learning

1. Introduction to Neural Networks and Course flow.mp4

04:45

2. Perceptron.mp4

09:44

3. Activation Functions.mp4

07:30

4. Creating Perceptron model in Python - Part 1.mp4

07:38

5. Creating Perceptron model in Python - Part 2.mp4

06:19

54. Neural Networks - Stacking cells to create network

1. Basic Terminologies.mp4

09:47

2. Gradient Descent.mp4

12:17

3. Back Propagation Part - 1.mp4

10:01

4. Back Propagation - Part 2.mp4

12:15

5. Some Important Concepts.mp4

12:44

6. Hyperparameter.mp4

08:19

55. ANN in Python

1. Keras and Tensorflow.mp4

03:04

2. Installing Tensorflow and Keras.mp4

04:01

3. Dataset for classification.mp4

07:19

4. Normalization and Test-Train split.mp4

06:04

5. Different ways to create ANN using Keras.mp4

01:58

6. Building the Neural Network using Keras.mp4

12:15

7. Compiling and Training the Neural Network model.mp4

10:59

8. Evaluating performance and Predicting using Keras.mp4

09:21

9. Building Neural Network for Regression Problem - Part 1.mp4

06:01

10. Building Neural Network for Regression Problem - Part 2.mp4

09:33

11. Building Neural Network for Regression Problem - Part 3.mp4

06:34

12. Using Functional API for complex architectures.mp4

12:40

13. Saving - Restoring Models and Using Callbacks - Part 1.mp4

12:58

14. Saving - Restoring Models and Using Callbacks - Part 2.mp4

06:49

15. Hyperparameter Tuning.mp4

09:05

56. CNN Basics

1. CNN Introduction.mp4

07:43

2. Stride.mp4

02:50

3. Padding.mp4

05:07

4. Filters and feature map.mp4

07:48

5. Channels.mp4

06:31

6. Pooling Layer.mp4

05:32

57. Creating CNN model in Python

1. CNN model in Python - Preprocessing.mp4

05:42

2. CNN model in Python - structure and Compile.mp4

06:24

3. CNN model in Python - Training and results.mp4

06:52

4. Comparison - Pooling vs Without Pooling in Python.mp4

06:20

58. Project Creating CNN model from scratch in Python

1. Project - Introduction.mp4

07:05

2. Data for the project.html

3. Project - Data Preprocessing in Python.mp4

09:16

4. Project - Training CNN model in Python.mp4

09:01

5. Project in Python - model results.mp4

03:07

59. Project Data Augmentation for avoiding overfitting

1. Project - Data Augmentation Preprocessing.mp4

06:44

2. Project - Data Augmentation Training and Results.mp4

06:25

60. Transfer Learning Basics

1. ILSVRC.mp4

04:08

2. LeNET.mp4

01:31

3. VGG16NET.mp4

02:00

4. GoogLeNet.mp4

02:52

5. Transfer Learning.mp4

05:15

6. Project - Transfer Learning - VGG16 - Part - 1.mp4

08:17

7. Project - Transfer Learning - VGG16 - Part - 2.mp4

08:08

8. Project - Transfer Learning - VGG16 - Part - 3.mp4

03:15

9. The final milestone!.mp4

01:33

61. Congratulations & about your certificate

1. Bonus Lecture.html

Description

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.

Who is this 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 science

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

More details

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 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 - SQLÂ 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.
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.

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

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Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.

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