DEV Community: miranda-1-gatwiri

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miranda-1-gatwiri — Sat, 25 Apr 2026 14:41:41 +0000

Joins and Window Functions: Ultimate SQL Playbook.

miranda-1-gatwiri — Fri, 06 Mar 2026 23:33:53 +0000

The relational databases are in fact strong in relation as well as ranking of the data. The two most handy in your SQL propensity are ruined in this manual.

The 7 Types of SQL Joins.

The joins are applied in the presentation of the similar information of other tables within the same group of outcomes.

INNER JOIN:

It does not refer to rows whereby the two tables will be joined.

LEFT JOIN:

It is a query that is used to give all the records of the left table and associated records of the right table. Where no match is found, the result is null.

RIGHT JOIN:

This right join connects all the rows of the right table with the connected rows of the left one.

FULL OUTER Join:

It includes all the information of the two tables and the instances where the contents are not alike.

SELF JOIN:

It is a table that joins itself to define the internal relations, e.g., an employee and his boss.

CROSS JOIN:

A query that is used to take out the entire rows of table 1 and cross them with all the rows of table 2 in a Cartesian product.

NATURAL JOIN:

This is an automatic join, and this join is built based on the similar name column. It is fast but has no security unless you are on guard.

Window Function.

Location processes operations perform the operations on a sequence of rows relative to the one row that is being processed, although it does not squash the result into a single output.

ROW NUMBER:

This is a type of functionality in which different rows are given progressive numbers that begin with the number one.

RANK():

Ranking has a weakness in that it contains ties (i.e., 1, 1, 3).

DENSE_RANK():

Organization of the data in a series with no intermediate between the distances (e.g., 1, 1, 2).

LEAD():

The following command is used to access the value of the following set row:

LAG():

This row can be obtained as the last row.

Key Concept Summary

The second will be to disaggregate your data using the window (PARTITION BY). An example to demonstrate that is a starting ROW number of 1 with a new purchase of a customer.

Joins vs. Functions:

Join is applied in order to add non-existing columns. The existing data can be collected in context (where the data is located or a summation of the data) using a window function.

Changing Messy Data, DAX, and Dashboards to a Course of action with Power BI

miranda-1-gatwiri — Sun, 08 Feb 2026 19:47:07 +0000

1. From Data Chaos to Clarity

All the data used by any analyst are unstructured spreadsheets, CSV files, databases, APIs, or web data; they are of varying format, not all of the values are represented, and there are duplicate rows or records. The first step in this anarchy is to change the mess into a semblance of a power query with the assistance of the power query editor of Power BI.
Analysts use it to:
clean/partition/format date/correct few columns/normalize raw data.
Merge and append datasets
Create connections and metadata.
Automate refresh processes
Lesson learned: To become an analyst, one should first be a data janitor, or a person who cleanses the dirty data and transforms it into a clean and trustworthy model.

2. The DAX Intelligent Model Construction.

When the data has been arranged, the formula language known as Data Analysis Expressions (DAX), the formula language of Power BI, can be invoked, after which the Power BI analysts can then create potent relationships and calculations.
*Common DAX tasks include:
*
Computed fields (i.e., profit margin = profit/revenue) are to be developed.
Develops based on KPIs such as ROI, growth, or retention rate per year.
The context filters in order to accomplish dynamic calculations.
Time intelligence comparison and analysis of trends.
Example: Profit Margin = Total Sales − Total Cost
Now I understand: DAX transforms data models in a stationary state into analyzing moving engines, which react to user input in real time.

3. Converting Analysis into Story: Dashboards.

The analysts visualize the insight through the use of dashboards and reports with the assistance of a model.
In Power BI, this means:
The development of a story based on the visual representation, such as maps, line charts, and KPIs.
Interactive reporting, slicers, filters, and drill-throughs.
Development of tailor-made themes and branding.
Both create and share in a secure way using the Power BI Service or Teams.
Significant lesson learned: Dashboards introduce availability of data and allow the parties involved an opportunity to research on insights in real time.

4. Driving Business Action

Action is what matters, but not the dashboard.
The application of Power BI to all the departments enables:

Executives to certify top level KPIs.
Managers in order to establish areas of performance.
Task forces to be impulsive.
Insights are actionable and give an opportunity to offer insights to decision-makers when they are needed the most and periodically updated, as well as when they are generated automatically and can be accessed with mobile devices.

5. Continuous Optimization

Analysts cannot relax after publication of reports.
They:

1. Gather feedback
2. by data set (minimize memory, ease querying)
3. According to the changing business need, new measures were integrated.

The Blueprint of Brilliance: Why the Heart of Power BI is Data Modeling.

miranda-1-gatwiri — Mon, 02 Feb 2026 19:30:59 +0000

One cannot create a Power BI report without an appropriate data model any more than a person can create a house without a blueprint. Everything may be okay on the outside, but when you gain weight, things will start bustling.
You should be able to master the art of the schema in order to ensure that your reports are fast, accurate, and modular. The Power BI Pro data modeling is destined here.

1. Fact vs. Dimension Table:

This is why in any data model the first thing that you should have is categorization of your tables. Suppose we are isolating the verbs and the nouns.
Fact Tables (The Verbs): It is information of fact—the events of the business. It may be transferred during sales, stock transfer or temperature. They are very high (millions of lines) and too narrow (not so many columns).
Dimension Tables (The Nouns): They will provide the outlay of the context. Who bought the product? Where was the store? What was the date? Filters, slices, and grouping dimensions are used.

2. The Gold Standard: Star Schema.

The last objective of Power BI is the star schema. It consists of a central fact table and a collection of dimension tables, which are joined to the central fact table using one-to-many (1:) relationships.
Why it’s the best choice:
Performance The horizontally distributed Power BI engine (VertiPaq) is most favorable to drive through the space of sparse fact tables and stratify them by dimension.
Simplicity: It is also simple to the data analysis expressions (DAX) because the relationship paths are simple as well.
Usability The end-user also finds it easy to move around due to the ease with which the fields radiate out of the center of interest.

3. Another Alternative that Might Have Occurred: Snowflake Schema.

An eventuality that occurs through the normalization of your dimension tables, i.e., splitting them into sub-dimensions, is known as a snowflake schema. You have no product table; you are a product subcategory category.
The Issue: This will save part of the storage (redundancy will be minimized), and it will require Power BI to make numerous joins so as to get a simple solution.
The Conclusion: Power BI is more preferable as far as Flat is concerned. You need to do what you can, and then you need to shred your snowflake branches into a one-dimensional table (one dimension, no cross-dimension) (which is known as denormalization).

4. The Relationships: The definition of the pipes.

Relationships are the logic gates that facilitate the data movement between the tables. To maintain your model in good condition, you have to observe the following 3 rules:
Cardinality: One-to-Many (1:1). Even when a relationship is not an alternative, many-to-many relationships should not be recommended since this may end up slowing down performance and misunderstanding of the outcome.
Direction of Cross-Filter: This is to be single (dimension to fact filtering). Both or bidirectional filtering should be done with a lot of care, as this can be one of the reasons for the circular dependencies and logic errors.
The Date Table: Power BI auto date/time forgotten. To ensure the correctness of the time-intelligence functions, it is necessary to make a special table date dimension.

5. The significance of good modeling.

In the long run, it will not be capable of modeling a report that is not modeled appropriately. The time that was devoted to the schema is due to the following reason:
Precision: It is possible to count the same numbers once or to represent the same number in each row unless they are interconnected in the appropriate way. Correct modeling will ensure that 1 + 1 = 2.
Speed: In an A-star schema, the engine can jump into unwanted data, i.e., your dashboards are displayed in milliseconds and not seconds.
Maintenance: A clean schema is a plug-and-play system in a case where your business is expanding and therefore requires additional data. A spaghetti model (also called a messy model or a flat file) cannot even make a single step of transforming a single variable.

jumia

miranda-1-gatwiri — Sun, 01 Feb 2026 19:43:03 +0000

PHASE 1: Data Cleaning

We had to scrub the raw data before it could be visualized, and the reason was that we needed to transform raw text into mathematical numbers. Here is my technical process:
De-Noise: Find and Replace (Ctrl + H) was used to remove Noise: ‘KSh’ and commas in the columns containing prices.
Normalizing Ranges: There were products with price ranges (e.g., 1,620 - 1,800). I mean, average these ranges to make us have only one numerical point in our charts.
Standardizing Ratings: Ratings were indicated to be 4.5/5. Text-to-Columns (use space as delimiting) was used to avoid all but the numeric 4.5.
Absolute Values: The negative numbers appeared in the column of raw reviews. The =ABS() function was used to make sure that all the reviews were positive numbers.
Deduplication: I have found and deleted 3 duplicates to make sure that our averages are not distorted by the presence of duplicates.

PHASE 2: Data Enrichment

Raw data finds out what has happened; enriched data is what found out where to look. To fuel our dashboard filters, I have added new logic to the dataset:
Categories of Ratings: I have used a new column based on an IFS formula to categorize products in:
Excellent: 4.0 – 5.0
Good: 3.0 – 3.9
Needs Attention: Below 3.0
Discount Tiers: I grouped the discounts into High (> 40), Medium (20-40), and Low (< 20).
Pricing Segments: I included a column of price ranges (i.e., under 1k, 1k-2.5k, above 2.5k) to enable a manager to restrict by budget.

PHASE 3: Analysis of Data

I conducted a statistical deep dive after cleaning and enriching. The following are the three Golden Insights that I discovered:
The Quality Driver: Ratings and Reviews 0.52.
Meaning: When the products are of high quality, they tend to have more interaction. The most successful thing about Jumia is quality.
The Discount Myth: I discovered that discounts and reviews had a correlation of 0.01.
Theory: Customer engagement is not necessarily fuelled by deep discounts. Customers do not trust low-price products that do not have reviews.
The Value Trap: I have found such products as the 5-piece cooking pot set that has a huge discount of 55 percent and a terrible rating of 2.1.
Meaning: Big discounts on these products are used to conceal low quality, thus resulting in large returns and dissatisfaction by the customers.

PHASE 4: Dashboard Design

I designed the dashboard as a one-stop shop of the marketing team of Jumia. It is constructed in three separate districts:
The KPI Tile Header: Four large numbers dynamically changing: The number of products sold (112), the average rating (3.89), the average discount (37%), and the quantity of reviews (723).
The Trend Zone: I have added two Scatter Plots. One illustrates Discount vs. Reviews (to establish the myth of the Discount) and the other Rating vs. Reviews (to establish the myth of Quality is King).
Interactive Slicers: I have included three slicers on the left side (Ratingg Category, Discount Category, and Price Range).
Functionality: When the Rating Slicer is clicked on the item 'Needs Attention', Jumia immediately displays the products with high discounts, but of low quality.
Final Recommendations of the Lead Analyst.
My analysis shows that I can recommend Jumia to:
Judge Rating more than Price: Within the search results, you should place more emphasis on items with a 4.5 or higher rating than on items with high discounts.
Fix the Ghost Products: In the 58 products that people have 0 reviews, you should provide the incentive of loyalty to the first few people who review the product to build the early trust.
Check the 'Value Traps': an item that has a higher discount of over 40 percent and rating of less than 3.0 must be put under the red flag of being checked up with the vendor.

excel assignment sheet

miranda-1-gatwiri — Sun, 01 Feb 2026 19:20:56 +0000

Part A -

Data Cleaning and Preparation.
The crude transactional data were refined and enhanced to make them accurate for subsequent analysis:

Duplicate Removal:

The similar rows were detected and deleted based on the whole data, which was the order ID. One duplicate record was processed.

Data Type Corrections:

The fields like "Order Date" and "Required Date" were changed to date fields. Quantity, UnitCost, and UnitPrice were numeric fields that were standardized to provide mathematical manipulations.

Missing Value Imputation:

All missing values in key columns such as City, Salesperson and Channel were processed. To illustrate, any record of entries that did not have a salesperson was coded as unnamed or unassigned to ensure data integrity.

Suspicious Data Flagging:

4 Unit Price: Checked on negative values (there are no such in the last set).
Discounts: 7 records were found where DiscountPct exceeded 30%. The high discount flag was used to mark these so as to review compliance.

Date Logic Correction: There were 34 cases when the RequiredDate was less than the OrderDate. These were adjusted in a required date column that was corrected (usually with a standard lead time added to the order date) so as to provide logical consistency.

Calculation of LeadTimeDays: A derivation column lead date was generated through computing the difference between the fixed required date and the order date. if(required date < ordered date, required date date+7))

Part B- Sales Operations Analysis.

There are a number of analytical frameworks that were implemented through Pivot Tables and formulaic calculations:

Cohort Analysis:

A summary table was developed to monitor the sum of gross revenue by country and time (years/quarters). This will enable the business to capture the revenue pattern of every market since the time it entered that market.

ABC Analysis:

SKUs in their product category were reviewed in terms of their contribution to gross revenue. This category aids in prioritizing high-value inventory (Category A).

Salesperson Productivity:

Each salesperson's was calculated using the following metrics:
Sum of Gross Revenue
Number of OrderID (Total orders)
Average Revenue per Order
Average Gross Profit

Channel Mix and Cannibalization:

The comparison of revenue share has been made among various regions in relation to channels such as Online, Retail, and Marketplace. The analysis is useful in determining whether online sales are increasing at the cost of the traditional retail in particular areas (e.g., the Americas or Africa).

Service Level Proxy:

The percentage of on-time orders was determined by country and category using the met helper column (which determines whether LeadTimeDays = 7 or not).

Price Compliance:

Region and Salesperson aggregated the high discount flag to calculate the proportion of orders that exceeded the 20% discount level to identify outliers such as A. Patel (Americas) or C. Otieno (Asia).

Part C Scenario Modeling (What-If).

One control panel was created that would replicate various business situations:
Input: Users have the option of modifying a global discount cap, unit cost inflation, and an uplift in quantity (sales boost).
Recalculation: The scenario profit column in the main data sheet dynamically recalculates itself based on these inputs, and the results could be compared to compare the current baseline profit with the scenario profit projected.

Part D Interactive Dashboard.

The last insights were represented in the form of visualization in a centralized dashboard:
KPIs: It has total revenue, gross profit, margin percentage, average order value, and on-time service level percentage.
Revenue per month: A line chart illustrating seasonality.
Profit by Region and Channel: A stacked column chart that visualizes the channels that bring the highest profit in every territory.
Top 10 SKUs: bar chart showing the main revenue earners.
Interactive Slicers: The dashboard will be linked to the slicers of Region Country, Channel, and ProductCategory, where users can drill down to particular units of information.

Introduction to MS Excel as a method of data analysis.

miranda-1-gatwiri — Sun, 25 Jan 2026 12:38:08 +0000

Have you ever heard of Microsoft Excel and wondered why it is used by so many people in data analytics? Never mind, this guide simplifies it in the easiest manner possible.
Microsoft Excel is among the most potent and easy-to-use tools to deal with data. Most analysts tend to begin with Excel before progressing to such tools as Python, SQL, or Power BI, and they have a good reason to do so. Excel enables a person to arrange, clean, analyze, and visualize data in the same place.
Excel can be used to transform raw data into valuable information, whether you are following sales, costs, survey results, or monitoring the output of your social media presence.
*What you can do to the data using Excel:
*

Storing and managing information.
Automatic results calculation.
Summarize and compare data
The visualization of trends should be done using charts and tables.
Swiftly identify trends and observations. What you are to learn in this article. At the end of this guide, you will be in a position to:

Learn the meaning and purpose of Microsoft Excel and the way it can be used to support data analytics.
Find rows, columns, and cells within a worksheet.
Create and systematize information in an uncontaminated table format.
Basic Excel functions to be used include SUM, AVERAGE, COUNT, and IF.
Sort, filter, and delete duplicates with cleaning data.
Pivot tables to summarize big data.
Simple charts should be created to create visual trends and comparisons.
Knowledge of the construction of Excel dashboards to use in reporting.
Learn to work with data as a novice. Some of the terms that you will come across before getting into it are: Cell—A row and column intersection (e.g., A1) Column—Vertical cells denoting A, B, C, and so on. Row—Horizontal cells indicated by 1, 2, 3, etc. Worksheet—Single workbook within an Excel file. Workbook A Microsoft Excel file with one or more worksheets. Dataset A structured table of data, a row and a column of data. Formula—An equation that carries out a calculation (e.g., =A1+B1) Function— An inbuilt formula like SUM or AVERAGE. Range—A set of chosen cells (e.g., A1:A10). Sort—Organizing information in a given arrangement. Filter—The display of only data with a specific set of conditions. Pivot Table—It is a tool that is used to summarize large amounts of data automatically. Chart— A chart is a graphical illustration of data. Dashboard— This is a one-page display of essential insights. Data cleaning involves preparing information to make it accurate and usable.

I Introduction to Microsoft Excel.

What Is Excel?
Microsoft Excel is a Microsoft spreadsheet application. It assists users in storing data, organizing data, and computing and analyzing data based on the tables composed of rows and columns.
With Excel, you can:
Calculate with the help of formulas and functions.
Sort and filter information
Create charts and graphs
Aggregate big data in pivot tables.
In simple words, Excel converts raw data into effective information upon which decisions can be made.
Knowledge of rows, columns, and cells.
Excel spreadsheets are constructed with:
Rows—Horizontal lines (1, 2, 3…)
Columns—Vertical lines (A, B, C…)
Cells—where rows and columns meet.
Each cell can contain:
Text (names, categories)
Numbers (sales, quantities)
Dates
Formulas
Example Dataset

2 DATA FUNCTIONS

SUM—Adds values in a range
=SUM(E2:E4)

AVERAGE—Finds the mean value
=AVERAGE(E2:E4)
Application: Means scores or sales per day.

COUNT -Counts numeric values
=COUNT(E2:E4)
Use case Number of transactions.

COUNTA—Counts non-empty cells
=COUNTA(E2:E4)
Application: Number of products or customers.

IF—Applies logical conditions
=IF(C2>10,"High","Low")
Use case: Categorization of performance (high/low, pass/fail).

3. Data Cleaning in Excel

Data usually requires cleaning before analysis.
Ordinary household chores are:
Duplicates Removal Data Removal Duplicates

Select the data (click anywhere inside your table or select the column).
Go to the Data tab.
Click Remove Duplicates.
Tick the column(s) you want Excel to check.
Click OK.

Filtering allows you to:
View only one product
Filter by a particular period of time.
Only selected records should be analyzed.
Accurate analysis is a result of clean data.

4. Pivot Tables: Potent Summaries.

Pivot tables provide an opportunity to summarize big data in seconds without complicated formulas.
You can answer such questions as
Total sales per product
Monthly sales trends
Quantity sold by category
Steps to Create a Pivot Table
Select your dataset
Click Insert → PivotTable
Choose where to place it
Drag fields to rows, columns, and values.
Example:
Rows → Product
Values → Sum of Total Sales
Everything is calculated automatically in Excel.

5. Charts in Excel

Numbers are transformed into visual charts, which are easy to comprehend.
Common Chart Types
Column Chart—Compare categories.
Line Chart—Display trends over time.
Pie Chart – Show proportions
How to Create a Chart
Select the data
Click Insert
Choose a chart type
Insights are better understood with the help of charts.

6. Dashboards in Excel

The dashboard is one page, displaying major insights.
A basic Excel dashboard can contain:
Total Sales (headline number)
Sum of price
Sum of quantity
Dashboards combine:
Pivot Tables
Charts
Slicers (filtering filters)
They assist in providing the decision-makers with a fast understanding of performance.

Conclusion

The best place to begin data analytics is Microsoft Excel. It allows you to:
Organize data efficiently
Calculate through the use of functions.
Clean and filter messy data
Respond to datasets using pivot tables.
Visualization of insights with the help of charts.
Develop report dashboards.
Being proficient in Excel provides a solid base in that an individual reaches higher tools such as Power BI, Python, or SQL.
The best of your analytics to you.

GIT BASICS: UNDERSTAND VERSIN CONTROL, TRACK CHANGES, PUSH AND PULL CODES FOR BEGINNERS

miranda-1-gatwiri — Sat, 24 Jan 2026 17:39:53 +0000

GIT BASH

Git simplifies version control by letting you save snapshots of your code, collaborate safely, and revert mistakes easily.

What is version control?

Version control is a system that helps you;

Track changes made to files over time.
Go back to previous versions if something breaks.
Collaborate with others without conflicts.

What are Git and GitHub?

Git is a tool installed on your computer that tracks changes in your project
GitHub is an online platform where you store Git repositories and collaborate with others.

Key terms to know:
A repository is a folder where Git tracks your project's history.
Working directory this is where you edit your files
The staging area is where you prepare file before you permanently save it
A commit is a snapshot of your project at a specific point in time

Setting Up Git

first confirm the version of the installed Git Bash.
git --version

Then configure with your name and email
git config --global user.name "name"
git config --global user.email "email"

Then generate an SSH key

An SSH key is a secure acces credential
How To Generate an SSH Key
ssh-keygen -t ed25519 -C "email"
ssh-keygen - an instruction of what we need to generate
-t - a type of key
ed225519 is a secure encryption method

C - a comment ssh-keygen -t ed25519 -C "email" It will generate two keys, which are private and public keys. We will use the public key to link the Git Bash with GitHub.

Create an ssh agent in Git Bash.

The agent is the holder of the key
eval "$(ssh-agent -s)"
eval —runs those commands in the Git Bash
ssh-agent—a program that mananges ssh keys
-s is a command for the shell

Add ssh agent to the Git Bash

ssh-add ~/.ssh/id_ed25519
ssh-add - a command of adding a key to the the ssh agent
~/.ssh/id_ed25519 - path to your private ssh key

Copy your public ssh key

cat ~/.ssh/id_ed25519.pub
cat - displays file content
.pub - public key file

Add ssh key to GitHub.

Go to GitHub
Click your profile picture → Settings
Click SSH and GPG keys
Click New SSH key
Title: any name
Paste the copied key
Click Add SSH key

Tracking your changes

Git tracks changes in three stages: working directory, staging area, and commit history.

Working directory

It holds all project files as they appear on your computer.
It edits the files

STEPS

Create a project folder
mkdir my-first-repo
Create a new folder
Move to the folder
cd my-first-repo
Enters the folder
Initialise Git
git init
Turns this folder into a Git repository

Add files
*touch README.md
*
creates a file.

Staging

When you make changes to a file, Git is aware of it, although it does not save it. The files you want to include in your next snapshot have to be staged.
_**git add README.md

Commit

After staging your files, you close them in with a message on what you have done.
Saves a snapshot of your project.
git commit -m " Add README.md file"

Add GitHub repository as a remote:
git remote add origin
git@github.com:username/repository-name.git
Then verify:
git remote -v

How to Push Code to GitHub.

Pushing is the act of sending your local commits to the remote server.
git push -u origin main
origin - github respository
main - branch name

How to Pull Code from GitHub

Pulling fetches and merges remote changes from Github.
git pull origin main

use this when :
Working on multiple choices.
Collaborating with a team
Updating your local project

Git workflow

GIT BASICS: UNDERSTAND VERSIN CONTROL, TRACK CHANGES, PUSH AND PULL CODES FOR BEGINNERS

miranda-1-gatwiri — Sat, 17 Jan 2026 21:30:22 +0000

GIT BASH

Git simplifies version control by letting you save snapshots of your code, collaborate safely, and revert mistakes easily.

What is version control?

Version control is a system that helps you;

Track changes made to files over time.
Go back to previous versions if something breaks.
Collaborate with others without conflicts.

What are Git and GitHub?

Git is a tool installed on your computer that tracks changes in your project
GitHub is an online platform where you store Git repositories and collaborate with others.

Key terms to know:
A repository is a folder where Git tracks your project's history.
Working directory this is where you edit your files
The staging area is where you prepare file before you permanently save it
A commit is a snapshot of your project at a specific point in time

Setting Up Git

first confirm the version of the installed Git Bash.
git --version

Then configure with your name and email
git config --global user.name "name"
git config --global user.email "email"

Then generate an SSH key

C - a comment ssh-keygen -t ed25519 -C "email" It will generate two keys, which are private and public keys. We will use the public key to link the Git Bash with GitHub.

Create an ssh agent in Git Bash.

The agent is the holder of the key
eval "$(ssh-agent -s)"
eval —runs those commands in the Git Bash
ssh-agent—a program that mananges ssh keys
-s is a command for the shell

Add ssh agent to the Git Bash

ssh-add ~/.ssh/id_ed25519
ssh-add - a command of adding a key to the the ssh agent
~/.ssh/id_ed25519 - path to your private ssh key

Copy your public ssh key

cat ~/.ssh/id_ed25519.pub
cat - displays file content
.pub - public key file

Add ssh key to GitHub.

Go to GitHub
Click your profile picture → Settings
Click SSH and GPG keys
Click New SSH key
Title: any name
Paste the copied key
Click Add SSH key

Tracking your changes

Git tracks changes in three stages: working directory, staging area, and commit history.

Working directory

It holds all project files as they appear on your computer.
It edits the files

STEPS

Create a project folder mkdir my-first-repo - Create a new folder
Move to the folder cd my-first-repo - Enters the folder

3.Initialise Git
git init
Turns this folder into a Git repository

Add files touch README.md - creates a file.

Staging

When you make changes to a file, Git is aware of it, although it does not save it. The files you want to include in your next snapshot have to be staged.
git add README.md

Commit

After staging your files, you close them in with a message on what you have done.
Saves a snapshot of your project.
git commit -m " Add README.md file"

Add the GitHub repository as a remote:
git remote add origin
git@github.com:username/repository-name.git
Then verify:
git remote -v

How to Push Code to GitHub.

Pushing is the act of sending your local commits to the remote server or sends your commit to the remote (like GitHub).
git push -u origin main
origin - GitHub repository
main - branch name

How to Pull Code from GitHub

Pull brings remote changes into your local.
git pull origin main

Use this when:

Working on multiple choices.
Collaborating with a team
Updating your local project

Git workflow

git init
git add . → Stage changes
git commit -m "first comment" → Save changes
git remote add origin → Upload to GitHub
git push -u origin main → Download updates

Conlusion.

Git may seem to be a daunting tool at its initial introduction, yet it is the greatest thing in the toolkit of a developer. You have already been taught 80 percent of the game by learning add, commit, push and pull, and can now apply it on the daily basis.

DEV Community: miranda-1-gatwiri

hi

Joins and Window Functions: Ultimate SQL Playbook.

The 7 Types of SQL Joins.

INNER JOIN:

LEFT JOIN:

RIGHT JOIN:

FULL OUTER Join:

SELF JOIN:

CROSS JOIN:

NATURAL JOIN:

Window Function.

ROW NUMBER:

RANK():

DENSE_RANK():

LEAD():

LAG():

Key Concept Summary

Joins vs. Functions:

Changing Messy Data, DAX, and Dashboards to a Course of action with Power BI

1. From Data Chaos to Clarity

2. The DAX Intelligent Model Construction.

3. Converting Analysis into Story: Dashboards.

4. Driving Business Action

5. Continuous Optimization

The Blueprint of Brilliance: Why the Heart of Power BI is Data Modeling.

1. Fact vs. Dimension Table:

2. The Gold Standard: Star Schema.

3. Another Alternative that Might Have Occurred: Snowflake Schema.

4. The Relationships: The definition of the pipes.

5. The significance of good modeling.

jumia

PHASE 1: Data Cleaning

PHASE 2: Data Enrichment

PHASE 3: Analysis of Data

PHASE 4: Dashboard Design

excel assignment sheet

Part A -

Duplicate Removal:

Data Type Corrections:

Missing Value Imputation:

Suspicious Data Flagging:

Date Logic Correction: There were 34 cases when the RequiredDate was less than the OrderDate. These were adjusted in a required date column that was corrected (usually with a standard lead time added to the order date) so as to provide logical consistency.

Part B- Sales Operations Analysis.

Cohort Analysis:

ABC Analysis:

Salesperson Productivity:

Channel Mix and Cannibalization:

Service Level Proxy:

Price Compliance:

Part C Scenario Modeling (What-If).

Part D Interactive Dashboard.

Introduction to MS Excel as a method of data analysis.

I Introduction to Microsoft Excel.

2 DATA FUNCTIONS

3. Data Cleaning in Excel

4. Pivot Tables: Potent Summaries.

5. Charts in Excel

6. Dashboards in Excel

Conclusion

GIT BASICS: UNDERSTAND VERSIN CONTROL, TRACK CHANGES, PUSH AND PULL CODES FOR BEGINNERS

GIT BASH

What is version control?

What are Git and GitHub?

Key terms to know:

Setting Up Git

Then generate an SSH key

Create an ssh agent in Git Bash.

Add ssh agent to the Git Bash

Copy your public ssh key

Add ssh key to GitHub.

Tracking your changes

Working directory

STEPS

Staging

Commit

How to Push Code to GitHub.

How to Pull Code from GitHub

Git workflow

GIT BASICS: UNDERSTAND VERSIN CONTROL, TRACK CHANGES, PUSH AND PULL CODES FOR BEGINNERS