[4-in-1 Bundle] Covers SQL, Knowledge viz utilizing Google’s Looker Studio, Machine Studying utilizing Python and ETL utilizing Alteryx
What you’ll study
Grasp SQL and carry out superior queries on relational databases.
Develop experience in information visualization utilizing Google’s Looker Studio and create interactive dashboards.
Discover machine studying algorithms and apply them to real-world information issues.
Grasp Python libraries resembling NumPy, Pandas, and Scikit-learn for information evaluation and modeling.
Perceive the ETL course of and learn to use Alteryx for information preparation and cleaning.
Learn to construct and consider regression and classification fashions
Develop expertise in information storytelling and talk insights successfully.
Description
For those who’re an information skilled trying to stage up your expertise and keep forward of the curve, that is the course for you. Would you like to have the ability to analyze and manipulate information with ease, create gorgeous visualizations, construct highly effective machine studying fashions, and streamline information workflows? Then be a part of us on this journey and turn out to be an information science rockstar.
On this course, you’ll:
- Develop experience in SQL, crucial language for working with relational databases
- Grasp information visualization utilizing Looker Studio, a robust platform for creating lovely and interactive dashboards
- Learn to construct machine studying fashions utilizing Python, a flexible and widely-used programming language
- Discover the world of ETL (Extract, Rework, Load) and information integration utilizing Alteryx, a preferred device for automating information workflows
Why study information science? It’s one of the crucial in-demand expertise in as we speak’s job market, with firms in all industries searching for professionals who can extract insights from information and make data-driven choices. On this course, you’ll acquire a deep understanding of the info science course of and the instruments and strategies utilized by prime information scientists.
All through the course, you’ll full a wide range of hands-on actions, together with SQL queries, information cleansing and preparation, constructing and evaluating machine studying fashions, and creating gorgeous visualizations utilizing Looker Studio. By the top of the course, you’ll have a portfolio of initiatives that show your information science expertise and a newfound confidence in your potential to work with information.
What makes us certified to show 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 within the World Analytics Consulting agency, we now have helped companies remedy their enterprise issues utilizing Analytics and we now have used our expertise to incorporate the sensible facets of enterprise analytics on this course. We’ve in-hand expertise in Enterprise Evaluation.
We’re additionally the creators of a few of the hottest on-line programs – with over 1,200,000 enrollments and hundreds of 5-star evaluations like these ones:
This is superb, i really like the very fact the all rationalization given may be understood by a layman – Joshua
Thanks Creator for this glorious course. You’re the finest and this course is price any value. – Daisy
Our Promise
Educating our college students is our job and we’re dedicated to it. In case you have any questions in regards to the course content material, apply sheet, or something associated to any matter, you possibly can all the time submit a query within the course or ship us a direct message.
Don’t miss out on this chance to turn out to be an information science skilled. Enroll now and begin your journey in direction of changing into a talented information scientist as we speak!
English
language
Content material
Introduction
Introduction
Set up and getting began
Putting in PostgreSQL and pgAdmin in your PC
It is a milestone!
If pgAdmin just isn’t opening…
Course Sources
Case Examine : Demo
Case Examine Half 1 – Enterprise issues
Case Examine Half 2 – How SQL is Used
Basic SQL statements
CREATE
INSERT
Import information from File
SELECT assertion
SELECT DISTINCT
WHERE
Logical Operators
UPDATE
DELETE
ALTER – Half 1
ALTER – Half 2
Restore and Again-up
Restore and Again-up
Debugging restoration points
Creating DB utilizing CSV information
Debugging abstract and Code for CSV information
Choice instructions: Filtering
IN
BETWEEN
LIKE
Choice instructions: Ordering
Aspect Lecture: Commenting in SQL
ORDER BY
LIMIT
Alias
AS
Combination Instructions
COUNT
SUM
AVERAGE
MIN & MAX
Group By Instructions
GROUP BY
HAVING
Conditional Assertion
CASE WHEN
JOINS
Introduction to Joins
Ideas of Becoming a member of and Combining Knowledge
Making ready the info
Internal Be a part of
Left Be a part of
Proper Be a part of
Full Outer Be a part of
Cross Be a part of
Intersect and Intersect ALL
Besides
Union
Subqueries
Subquery in WHERE clause
Subquery in FROM clause
Subquery in SELECT clause
Views and Indexes
VIEWS
INDEX
String Capabilities
LENGTH
UPPER LOWER
REPLACE
TRIM, LTRIM, RTRIM
CONCATENATION
SUBSTRING
LIST AGGREGATION
Mathematical Capabilities
CEIL & FLOOR
RANDOM
SETSEED
ROUND
POWER
Date-Time Capabilities
CURRENT DATE & TIME
AGE
EXTRACT
PATTERN (STRING) MATCHING
PATTERN MATCHING BASICS
ADVANCE PATTERN MATCHING – Half 1
ADVANCE PATTERN MATCHING – Half 2
Window Capabilities
Introduction to Window capabilities
Introduction to Row quantity
Implementing Row quantity in SQL
RANK and DENSERANK
NTILE operate
AVERAGE operate
COUNT
SUM TOTAL
RUNNING TOTAL
LAG and LEAD
COALESCE operate
COALESCE operate
Knowledge Sort conversion capabilities
Changing Numbers/ Date to String
Changing String to Numbers/ Date
Consumer Entry Management Capabilities
Consumer Entry Management – Half 1
Consumer Entry Management – Half 2
Nail that Interview!
Tablespace
PRIMARY KEY & FOREIGN KEY
ACID compliance
Truncate
Looker Studio
Introduction
Why Knowledge Studio?
Terminologies & Theoretical ideas for Knowledge Studio
Knowledge Studio House Display screen & Dataset vs Knowledge Supply
Construction of Enter information
Dimensions vs Measures (new definition)
Sensible half begins right here
Opening Knowledge Studio and making ready information
Including an information supply
Managing added information supply
Charts to spotlight numbers
Knowledge Desk
Styling tab for information desk
Scorecards
Charts for evaluating classes : Bar charts and stacked charts
Easy Bar and Column chart
Stacked Column chart
Charting maps of a rustic, continent or a area – Geomaps
GeoMap
Charts to spotlight developments : Time sequence, Line and Space charts
Time Collection
Replace to Time Collection chart
Line Chart and Combo Chart
Spotlight contribution to complete: Pie chart & Donut Chart
Pie Chart and Donut Chart
Stacked Space Charts
Up to date information for space charts
Relationship between two or extra variables: Scatterplots
Scatter Plots and Bubble charts
Aggregating on two dimensions: Pivot tables
Pivot tables for cross tabulation
All a few single Metric: Bullet Chart
Bullet Chart
Chart for highlighting heirarchy: TreeMap
TreeMaps
Branding a Report
Branding a Report: Model Brand and Firm Particulars
Model colours for report branding
Giving the ability to filter Knowledge to viewers
Filter controls for viewers
Add Movies, Suggestions kind and many others. to your Report
URL Embed to incorporate exterior content material
Generally information is in a number of tables
Mixing information from a number of tables
Several types of Joins whereas mixing information
Sharing and collaborating on Knowledge Studio report
Downloading report as PDF and Web page Administration
Sharing report and Knowledge Credentials
Sharing report utilizing a hyperlink
Scheduling emails
Embeding report on Web site
Charting Finest Practices
Highlighting chart message
Eliminating Distractions from the Graph
Avoiding litter
Avoiding the Spaghetti plot
Machine Studying in Python
Introduction
Organising Python and Jupyter pocket book
Putting in Python and Anaconda
Opening Jupyter Pocket book
Introduction to Jupyter
Arithmetic operators in Python: Python Fundamentals
Strings in Python: Python Fundamentals
Lists, Tuples and Directories: Python Fundamentals
Working with Numpy Library of Python
Working with Pandas Library of Python
Working with Seaborn Library of Python
Fundamentals of statistics
Forms of Knowledge
Forms of Statistics
Describing information Graphically
Measures of Facilities
Measures of Dispersion
Introduction to Machine Studying
Introduction to Machine Studying
Constructing a Machine Studying Mannequin
Knowledge Preprocessing
Gathering Enterprise Data
Knowledge Exploration
The Dataset and the Knowledge Dictionary
Importing Knowledge in Python
Univariate evaluation and EDD
EDD in Python
Outlier Remedy
Outlier Remedy in Python
Lacking Worth Imputation
Lacking Worth Imputation in Python
Seasonality in Knowledge
Bi-variate evaluation and Variable transformation
Variable transformation and deletion in Python
Non-usable variables
Dummy variable creation: Dealing with qualitative information
Dummy variable creation in Python
Correlation Evaluation
Correlation Evaluation in Python
Linear Regression
The Downside Assertion
Fundamental Equations and Abnormal Least Squares (OLS) technique
Assessing accuracy of predicted coefficients
Assessing Mannequin Accuracy: RSE and R squared
Easy Linear Regression in Python
A number of Linear Regression
The F – statistic
Deciphering outcomes of Categorical variables
A number of Linear Regression in Python
Check-train break up
Bias Variance trade-off
Check prepare break up in Python
Regression fashions aside from OLS
Subset choice strategies
Shrinkage strategies: Ridge and Lasso
Ridge regression and Lasso in Python
Heteroscedasticity
Introduction to the classification Fashions
Three classification fashions and Knowledge set
Importing the info into Python
The issue statements
Why can’t we use Linear Regression?
Logistic Regression
Logistic Regression
Coaching a Easy Logistic Mannequin in Python
Results of Easy Logistic Regression
Logistic with a number of predictors
Coaching a number of predictor Logistic mannequin in Python
Confusion Matrix
Creating Confusion Matrix in Python
Evaluating efficiency of mannequin
Evaluating mannequin efficiency in Python
Linear Discriminant Evaluation (LDA)
Linear Discriminant Evaluation
LDA in Python
Ok-Nearest Neighbors classifier
Check-Prepare Cut up
Check-Prepare Cut up in Python
Ok-Nearest Neighbors classifier
Ok-Nearest Neighbors in Python: Half 1
Ok-Nearest Neighbors in Python: Half 2
Evaluating outcomes from 3 fashions
Understanding the outcomes of classification fashions
Abstract of the three fashions
Easy Resolution Bushes
Introduction to Resolution timber
Fundamentals of Resolution Bushes
Understanding a Regression Tree
The stopping standards for controlling tree progress
Importing the Knowledge set into Python
Lacking worth remedy in Python
Dummy Variable Creation in Python
Dependent- Unbiased Knowledge break up in Python
Check-Prepare break up in Python
Creating Resolution tree in Python
Evaluating mannequin efficiency in Python
Plotting determination tree in Python
Pruning a tree
Pruning a tree in Python
Easy Classification Tree
Classification tree
The Knowledge set for Classification downside
Classification tree in Python : Preprocessing
Classification tree in Python : Coaching
Benefits and Disadvantages of Resolution Bushes
Ensemble method 1 – Bagging
Ensemble method 1 – Bagging
Ensemble method 1 – Bagging in Python
Ensemble method 2 – Random Forests
Ensemble method 2 – Random Forests
Ensemble method 2 – Random Forests in Python
Utilizing Grid Search in Python
Ensemble method 3 Boosting
Boosting
Ensemble method 3a – Boosting in Python
Ensemble method 3b – AdaBoost in Python
Ensemble method 3c – XGBoost in Python
Alteryx
The Downside Assertion
Case research and Alteryx Set up
Putting in Alteryx
Alteryx Interface
DATA EXTRACTION: Extracting tabular information
Manually coming into information into Alteryx
Importing Knowledge from a CSV (Comma Separated Values) file
Importing Knowledge from a TXT (textual content) file
Importing Knowledge from an Excel file
Importing Knowledge from a ZIP file
Importing Knowledge from a number of information in a folder
DATA EXTRACTION: Extracting non-tabular information
Possible Difficulty with Extraction from XML
Extracting from XML
Extracting from an SQL desk
Plan for importing gross sales Knowledge
Putting in PostgreSQL and pgAdmin in your PC
Creating Gross sales desk in SQL
Extracting from an SQL desk
Storing and Retrieving Knowledge Cloud storage
Storing Knowledge on AWS S3
Importing information from AWS S3
Merging Knowledge Streams
Union device – Merging Buyer Knowledge
Knowledge Cleaning and enhancing information high quality
Discover and Exchange Software
Knowledge Cleansing Software
Autofield and Choose Software – For controlling Area order and information kind
Merging Gross sales and Product information
Choose and Distinctive Instruments- For Eradicating duplicates from product information
Date Parse – Altering Date format
Choose and union – Merging Gross sales information
Sampling Knowledge
Choose Information Software
Pattern Software
Random % Pattern Software
Prepare-Validation-Check Cut up sampling
Knowledge Preparation
Multifield binning and Tile Software – To create buyer age classes
Method Software – Conditional Method for giving class titles
Type device – Sorting buyer Knowledge primarily based on ID
Method Software – Gross sales order date & ship date
Multifield Method device – Changing a number of forex fields
Filtering and Sorting – Optimistic variety of days
Textual content to Columns – Splitting Product ID into 3 columns
Outputting Cleaned Knowledge
Outputting Clear Buyer & Product Knowledge
Merging tables to create a datamart
The Becoming a member of Software – Including buyer and Product information to Gross sales desk
Extracting extra information from the Date values
Performing Analytics/ Transformation on Datamart
The Summarize device
Working Complete Software
Crosstab device for creating Pivot tables
Transpose Software – the other of Cross Tab device
The Rely device
Making a report in Alteryx
Introduction to Reporting
Interactive Chart device – Bar chart to point out region-wise gross sales
Interactive Chart device – Line chart to point out Gross sales pattern
Desk Software – Formatting the Pivot desk
Textual content Software – Including static textual content to a report
Visible Structure device – Arranging charts, textual content and tables in a report
Header device – Including header in a report
Footer device – Including footer to a report
Rendering device – rendering report as a PDF, HTML or PNG
E mail Software – Sending electronic mail with Alteryx
Picture device – Including picture to a report
Structure device – Arranging charts, textual content or tables in a report
Scheduling a workflow in Alteryx
Schedule and Automate Alteryx workflow
Congratulations & about your certificates
Various to Alteryx
The ultimate milestone!
Bonus Lecture
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