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Data Science Skillpath: SQL, ML, Looker Studio & Alteryx (05/2024) - lovewarez - 05-26-2024
![[Image: 40d712c186e00e8e15122f04b966438d.jpg]](https://i123.fastpic.org/big/2024/0521/8d/40d712c186e00e8e15122f04b966438d.jpg)
Data Science Skillpath: SQL, ML, Looker Studio & Alteryx (05/2024)
Video : H265 1280x720 16:9 30 fps 800 kbps | Audio : AAC 48 kHz 128 kbps 2 channels | Duration: 31h | 8.04 GB
Genre: eLearning | Language: English
Video lessons in English, English subtitles added. Added lost materials.
If you're a data professional looking to level up your skills and stay ahead of the curve, this is the course for you. Do you want to be able to analyze and manipulate data with ease, create stunning visualizations, build powerful machine learning models, and streamline data workflows? Then join us on this journey and become a data science rockstar.
In this course, you will:
Develop expertise in SQL, the most important language for working with relational databases
Master data visualization using Looker Studio, a powerful platform for creating beautiful and interactive dashboards
Learn how to build machine learning models using Python, a versatile and widely-used programming language
Explore the world of ETL (Extract, Transform, Load) and data integration using Alteryx, a popular tool for automating data workflows
Why learn about data science? It's one of the most in-demand skills in today's job market, with companies in all industries looking for professionals who can extract insights from data and make data-driven decisions. In this course, you'll gain a deep understanding of the data science process and the tools and techniques used by top data scientists.
Throughout the course, you'll complete a variety of hands-on activities, including SQL queries, data cleaning and preparation, building and evaluating machine learning models, and creating stunning visualizations using Looker Studio. By the end of the course, you'll have a portfolio of projects that demonstrate your data science skills and a newfound confidence in your ability to work with data.
Content
Chapter 1-Introduction
Chapter 2-Installation and getting started
Chapter 3-Fundamental SQL Statements
Chapter 4-Restore and Back up
Chapter 5-Selection commands Filtering
Chapter 6-Selection commands Ordering
Chapter 7-Alias
Chapter 8-Aggregate Commands
Chapter 9-Group by Commands
Chapter 10-Conditional Statement
Chapter 11-JOINS
Chapter 12-Subqueries
Chapter 13-Fun way to practice SQL
Chapter 14-Views and Indexes
Chapter 15-String Functions
Chapter 16-Mathematical Functions
Chapter 17-Date Time Functions
Chapter 18-Pattern (String) Matching
Chapter 19-Window Functions
Chapter 20-COALESCE Function
Chapter 21-Data Type Conversion Functions
Chapter 22-User Access Control Functions
Chapter 23-Nail that interview
Chapter 24-Looker Studio
Chapter 25-Terminologies and Theoretica concepts for Data Studio
Chapter 26-Practical part begins here
Chapter 27-Charts to highlight numbers
Chapter 28-Charts for comparing categories Bar charts and stacked charts
Chapter 29-Charting maps of a country continent or a region Geomaps
Chapter 30-Charts to highlight trends Time series Line and Area charts
Chapter 31-Highlight contribution to total Pie chart and Donut Chart
Chapter 32-Relationship between two or more variables Scatterplots
Chapter 33-Aggregating on two dimensions Pivot tables
Chapter 34-All about a single Metric Bullet Chart
Chapter 35-Charts for highlighting heirarchy TreeMap
Chapter 36-Branding a Report
Chapter 37-Giving the power to filter Data to viewers
Chapter 38-Add Videos Feedback form etc to your Report
Chapter 39-Sometimes data is in multiple tables
Chapter 40-Sharing and collaborating on Data Studio report
Chapter 41-Charting Best Practices
Chapter 42-Machine learning in Python
Chapter 43-Setting up Python and Jupyter notebook
Chapter 44-Integrating ChatGPT with Python
Chapter 45-Basics of statistics
Chapter 46-Introduction to Machine Learning
Chapter 47-Data Preprocessing
Chapter 48-Linear Regression
Chapter 49-Introduction to the classification Models
Chapter 50-Logistic Regression
Chapter 51-Linear Discriminant Analysis (LDA)
Chapter 52-K Nearest Neighbors classifier
Chapter 53-Comparing results from 3 models
Chapter 54-Simple Decision Trees
Chapter 55-Simple Classification Tree
Chapter 56-Ensemble technique 1 Bagging
Chapter 57-Ensemble technique 2 Random Forests
Chapter 58-Ensemble technique 3 Boosting
Chapter 59-Alteryx
Chapter 60-Case study and Alteryx Installation
Chapter 61-DATA EXTRACTION Extracting tabular data
Chapter 62-DATA EXTRACTION Extracting non tabular data
Chapter 63-Extracting from an SQL table
Chapter 64-Storing and Retrieving Data Cloud storage
Chapter 65-Merging Data Streams
Chapter 66-Data Cleansing and Improving data quality
Chapter 67-Merging Sales and Product data
Chapter 68-Sampling Data
Chapter 69-Data Preparation
Chapter 70-Outputting Cleaned Data
Chapter 71-Merging tables to create a datamart
Chapter 72-Performing Analytics Transformation on Datamart
Chapter 73-Creating a report in Alteryx
Chapter 74-Scheduling a workflow in Alteryx
Chapter 75-Congratulations and about your certificate