Data Science Skillpath: Sql, Ml, Looker Studio & Alteryx

[4-in-1 Bundle] Covers SQL, Data viz using Google's Looker Studio, Machine Learning using Python and ETL using Alteryx

What you'll learn

Master SQL and perform advanced queries on relational databases.

Develop expertise in data visualization using Google's Looker Studio and create interactive dashboards.

Explore machine learning algorithms and apply them to real-world data problems.

Master Python libraries such as NumPy, Pandas, and Scikit-learn for data analysis and modeling.

Understand the ETL process and learn how to use Alteryx for data preparation and cleansing.

Learn how to build and evaluate regression and classification models

Develop skills in data storytelling and communicate insights effectively.

Requirements

A PC with internet connection. Installation instructions for all tools used are covered in the course.

Description

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 databasesMaster data visualization using Looker Studio, a powerful platform for creating beautiful and interactive dashboardsLearn how to build machine learning models using Python, a versatile and widely-used programming languageExplore the world of ETL (Extract, Transform, Load) and data integration using Alteryx, a popular tool for automating data workflowsWhy 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.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 science expert. Enroll now and start your journey towards becoming a skilled data scientist today!

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: Looker Studio

Lecture 90 Introduction

Lecture 91 Why Data Studio?

Section 25: Terminologies & Theoretical concepts for Data Studio

Lecture 92 Data Studio Home Screen & Dataset vs Data Source

Lecture 93 Structure of Input data

Lecture 94 Dimensions vs Measures (new definition)

Section 26: Practical part begins here

Lecture 95 Opening Data Studio and preparing data

Lecture 96 Adding a data source

Lecture 97 Managing added data source

Section 27: Charts to highlight numbers

Lecture 98 Data Table

Lecture 99 Styling tab for data table

Lecture 100 Scorecards

Section 28: Charts for comparing categories : Bar charts and stacked charts

Lecture 101 Simple Bar and Column chart

Lecture 102 Stacked Column chart

Section 29: Charting maps of a country, continent or a region - Geomaps

Lecture 103 GeoMap

Section 30: Charts to highlight trends : Time series, Line and Area charts

Lecture 104 Time Series

Lecture 105 Update to Time Series chart

Lecture 106 Line Chart and Combo Chart

Section 31: Highlight contribution to total: Pie chart & Donut Chart

Lecture 107 Pie Chart and Donut Chart

Lecture 108 Stacked Area Charts

Lecture 109 Updated data for area charts

Section 32: Relationship between two or more variables: Scatterplots

Lecture 110 Scatter Plots and Bubble charts

Section 33: Aggregating on two dimensions: Pivot tables

Lecture 111 Pivot tables for cross tabulation

Section 34: All about a single Metric: Bullet Chart

Lecture 112 Bullet Chart

Section 35: Chart for highlighting heirarchy: TreeMap

Lecture 113 TreeMaps

Section 36: Branding a Report

Lecture 114 Branding a Report: Brand Logo and Company Details

Lecture 115 Brand colors for report branding

Section 37: Giving the power to filter Data to viewers

Lecture 116 Filter controls for viewers

Section 38: Add Videos, Feedback form etc. to your Report

Lecture 117 URL Embed to include external content

Section 39: Sometimes data is in multiple tables

Lecture 118 Blending data from multiple tables

Lecture 119 Different types of Joins while blending data

Section 40: Sharing and collaborating on Data Studio report

Lecture 120 Downloading report as PDF and Page Management

Lecture 121 Sharing report and Data Credentials

Lecture 122 Sharing report using a link

Lecture 123 Scheduling emails

Lecture 124 Embeding report on Website

Section 41: Charting Best Practices

Lecture 125 Highlighting chart message

Lecture 126 Eliminating Distractions from the Graph

Lecture 127 Avoiding clutter

Lecture 128 Avoiding the Spaghetti plot

Section 42: Machine Learning in Python

Lecture 129 Introduction

Section 43: Setting up Python and Jupyter notebook

Lecture 130 Installing Python and Anaconda

Lecture 131 Opening Jupyter Notebook

Lecture 132 Introduction to Jupyter

Lecture 133 Arithmetic operators in Python: Python Basics

Lecture 134 Strings in Python: Python Basics

Lecture 135 Lists, Tuples and Directories: Python Basics

Lecture 136 Working with Numpy Library of Python

Lecture 137 Working with Pandas Library of Python

Lecture 138 Working with Seaborn Library of Python

Section 44: Basics of statistics

Lecture 139 Types of Data

Lecture 140 Types of Statistics

Lecture 141 Describing data Graphically

Lecture 142 Measures of Centers

Lecture 143 Measures of Dispersion

Section 45: Introduction to Machine Learning

Lecture 144 Introduction to Machine Learning

Lecture 145 Building a Machine Learning Model

Section 46: Data Preprocessing

Lecture 146 Gathering Business Knowledge

Lecture 147 Data Exploration

Lecture 148 The Dataset and the Data Dictionary

Lecture 149 Importing Data in Python

Lecture 150 Univariate analysis and EDD

Lecture 151 EDD in Python

Lecture 152 Outlier Treatment

Lecture 153 Outlier Treatment in Python

Lecture 154 Missing Value Imputation

Lecture 155 Missing Value Imputation in Python

Lecture 156 Seasonality in Data

Lecture 157 Bi-variate analysis and Variable transformation

Lecture 158 Variable transformation and deletion in Python

Lecture 159 Non-usable variables

Lecture 160 Dummy variable creation: Handling qualitative data

Lecture 161 Dummy variable creation in Python

Lecture 162 Correlation Analysis

Lecture 163 Correlation Analysis in Python

Section 47: Linear Regression

Lecture 164 The Problem Statement

Lecture 165 Basic Equations and Ordinary Least Squares (OLS) method

Lecture 166 Assessing accuracy of predicted coefficients

Lecture 167 Assessing Model Accuracy: RSE and R squared

Lecture 168 Simple Linear Regression in Python

Lecture 169 Multiple Linear Regression

Lecture 170 The F - statistic

Lecture 171 Interpreting results of Categorical variables

Lecture 172 Multiple Linear Regression in Python

Lecture 173 Test-train split

Lecture 174 Bias Variance trade-off

Lecture 175 Test train split in Python

Lecture 176 Regression models other than OLS

Lecture 177 Subset selection techniques

Lecture 178 Shrinkage methods: Ridge and Lasso

Lecture 179 Ridge regression and Lasso in Python

Lecture 180 Heteroscedasticity

Section 48: Introduction to the classification Models

Lecture 181 Three classification models and Data set

Lecture 182 Importing the data into Python

Lecture 183 The problem statements

Lecture 184 Why can't we use Linear Regression?

Section 49: Logistic Regression

Lecture 185 Logistic Regression

Lecture 186 Training a Simple Logistic Model in Python

Lecture 187 Result of Simple Logistic Regression

Lecture 188 Logistic with multiple predictors

Lecture 189 Training multiple predictor Logistic model in Python

Lecture 190 Confusion Matrix

Lecture 191 Creating Confusion Matrix in Python

Lecture 192 Evaluating performance of model

Lecture 193 Evaluating model performance in Python

Section 50: Linear Discriminant Analysis (LDA)

Lecture 194 Linear Discriminant Analysis

Lecture 195 LDA in Python

Section 51: K-Nearest Neighbors classifier

Lecture 196 Test-Train Split

Lecture 197 Test-Train Split in Python

Lecture 198 K-Nearest Neighbors classifier

Lecture 199 K-Nearest Neighbors in Python: Part 1

Lecture 200 K-Nearest Neighbors in Python: Part 2

Section 52: Comparing results from 3 models

Lecture 201 Understanding the results of classification models

Lecture 202 Summary of the three models

Section 53: Simple Decision Trees

Lecture 203 Introduction to Decision trees

Lecture 204 Basics of Decision Trees

Lecture 205 Understanding a Regression Tree

Lecture 206 The stopping criteria for controlling tree growth

Lecture 207 Importing the Data set into Python

Lecture 208 Missing value treatment in Python

Lecture 209 Dummy Variable Creation in Python

Lecture 210 Dependent- Independent Data split in Python

Lecture 211 Test-Train split in Python

Lecture 212 Creating Decision tree in Python

Lecture 213 Evaluating model performance in Python

Lecture 214 Plotting decision tree in Python

Lecture 215 Pruning a tree

Lecture 216 Pruning a tree in Python

Section 54: Simple Classification Tree

Lecture 217 Classification tree

Lecture 218 The Data set for Classification problem

Lecture 219 Classification tree in Python : Preprocessing

Lecture 220 Classification tree in Python : Training

Lecture 221 Advantages and Disadvantages of Decision Trees

Section 55: Ensemble technique 1 - Bagging

Lecture 222 Ensemble technique 1 - Bagging

Lecture 223 Ensemble technique 1 - Bagging in Python

Section 56: Ensemble technique 2 - Random Forests

Lecture 224 Ensemble technique 2 - Random Forests

Lecture 225 Ensemble technique 2 - Random Forests in Python

Lecture 226 Using Grid Search in Python

Section 57: Ensemble technique 3 Boosting

Lecture 227 Boosting

Lecture 228 Ensemble technique 3a - Boosting in Python

Lecture 229 Ensemble technique 3b - AdaBoost in Python

Lecture 230 Ensemble technique 3c - XGBoost in Python

Section 58: Alteryx

Lecture 231 The Problem Statement

Section 59: Case study and Alteryx Installation

Lecture 232 Installing Alteryx

Lecture 233 Alteryx Interface

Section 60: DATA EXTRACTION: Extracting tabular data

Lecture 234 Manually entering data into Alteryx

Lecture 235 Importing Data from a CSV (Comma Separated Values) file

Lecture 236 Importing Data from a TXT (text) file

Lecture 237 Importing Data from an Excel file

Lecture 238 Importing Data from a ZIP file

Lecture 239 Importing Data from multiple files in a folder

Section 61: DATA EXTRACTION: Extracting non-tabular data

Lecture 240 Probable Issue with Extraction from XML

Lecture 241 Extracting from XML

Section 62: Extracting from an SQL table

Lecture 242 Plan for importing sales Data

Lecture 243 Installing PostgreSQL and pgAdmin in your PC

Lecture 244 Creating Sales table in SQL

Lecture 245 Extracting from an SQL table

Section 63: Storing and Retrieving Data Cloud storage

Lecture 246 Storing Data on AWS S3

Lecture 247 Importing data from AWS S3

Section 64: Merging Data Streams

Lecture 248 Union tool - Merging Customer Data

Section 65: Data Cleansing and improving data quality

Lecture 249 Find and Replace Tool

Lecture 250 Data Cleaning Tool

Lecture 251 Autofield and Select Tool - For controlling Field order and data type

Section 66: Merging Sales and Product data

Lecture 252 Select and Unique Tools- For Removing duplicates from product data

Lecture 253 Date Parse - Changing Date format

Lecture 254 Select and union - Merging Sales data

Section 67: Sampling Data

Lecture 255 Select Records Tool

Lecture 256 Sample Tool

Lecture 257 Random Percent Sample Tool

Lecture 258 Train-Validation-Test Split sampling

Section 68: Data Preparation

Lecture 259 Multifield binning and Tile Tool - To create customer age categories

Lecture 260 Formula Tool - Conditional Formula for giving category titles

Lecture 261 Sort tool - Sorting customer Data based on ID

Lecture 262 Formula Tool - Sales order date & ship date

Lecture 263 Multifield Formula tool - Converting multiple currency fields

Lecture 264 Filtering and Sorting - Positive number of days

Lecture 265 Text to Columns - Splitting Product ID into 3 columns

Section 69: Outputting Cleaned Data

Lecture 266 Outputting Clean Customer & Product Data

Section 70: Merging tables to create a datamart

Lecture 267 The Joining Tool - Adding customer and Product data to Sales table

Lecture 268 Extracting more info from the Date values

Section 71: Performing Analytics/ Transformation on Datamart

Lecture 269 The Summarize tool

Lecture 270 Running Total Tool

Lecture 271 Crosstab tool for creating Pivot tables

Lecture 272 Transpose Tool - the opposite of Cross Tab tool

Lecture 273 The Count tool

Section 72: Creating a report in Alteryx

Lecture 274 Introduction to Reporting

Lecture 275 Interactive Chart tool - Bar chart to show region-wise sales

Lecture 276 Interactive Chart tool - Line chart to show Sales trend

Lecture 277 Table Tool - Formatting the Pivot table

Lecture 278 Text Tool - Adding static text to a report

Lecture 279 Visual Layout tool - Arranging charts, text and tables in a report

Lecture 280 Header tool - Adding header in a report

Lecture 281 Footer tool - Adding footer to a report

Lecture 282 Rendering tool - rendering report as a PDF, HTML or PNG

Lecture 283 Email Tool - Sending email with Alteryx

Lecture 284 Image tool - Adding image to a report

Lecture 285 Layout tool - Arranging charts, text or tables in a report

Section 73: Scheduling a workflow in Alteryx

Lecture 286 Schedule and Automate Alteryx workflow

Section 74: Congratulations & about your certificate

Lecture 287 Alternative to Alteryx

Lecture 288 The final milestone!

Lecture 289 Bonus Lecture

Recent graduates or job seekers who want to break into the field of data science and acquire a comprehensive skillset.,Small business owners who want to learn how to effectively analyze data and create reports to inform their business decisions.,Analysts who want to enhance their skills in data management and visualization using SQL, Looker Studio, and Alteryx