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A few days back, 2 Data Analyst newbies showed me mistakes they encountered during their analysis process. It dawned on me that lots of times, we don't diligently follow through the data analysis process, especially the cleaning phase.
In this short🧵, I break down this process.
In this short🧵, I break down this process.
PHASE 1: ASK QUESTIONS
The first step in the analysis process is to formulate a problem to solve or a question to answer, such as: are customers aged 21 to 30 more likely to churn than customers aged 41 to 50? As analysts, we must be very curious, we must ASK QUESTIONS!
The first step in the analysis process is to formulate a problem to solve or a question to answer, such as: are customers aged 21 to 30 more likely to churn than customers aged 41 to 50? As analysts, we must be very curious, we must ASK QUESTIONS!
PHASE 2: Collect & Store Data
Next, we need to collect and store the necessary data, which could require the use of a database or a spreadsheet. For large datasets, we store them in a database. For datasets shy of a million records, spreadsheet software like Excel can be used.
Next, we need to collect and store the necessary data, which could require the use of a database or a spreadsheet. For large datasets, we store them in a database. For datasets shy of a million records, spreadsheet software like Excel can be used.
Day 10 of #100dayswithMachineLearning
Topic - Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer
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Topic - Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer
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Do you want to start a career in the field of #DataScience #MachineLearning but confused about the different job titles available in this Data-Driven career and the appropriate skill sets needed to excel in one
#Data powers today's world ransformed radically by #data @avizyt
#Data powers today's world ransformed radically by #data @avizyt
This article aims to demystify the different job titles for #datascience and #machinelearning based career paths. We would look into some job titles such as #DataAnalyst, Data Scientist, Data Engineer, and Machine Learning #Engineer
Blog Link - medium.com/campusx/explor…
Blog Link - medium.com/campusx/explor…
SQL JOINS for beginners { Must Read }
Joins are the most fundamental concepts in SQL and having a clear understanding of Joins is very essential to write good SQL queries.
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Joins are the most fundamental concepts in SQL and having a clear understanding of Joins is very essential to write good SQL queries.
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Topics -
✅What are JOINS in SQL
✅Inner Joins in SQL
✅Left Join in SQL
✅Right Join in SQL
✅What are JOINS in SQL
✅Inner Joins in SQL
✅Left Join in SQL
✅Right Join in SQL
✅FULL OUTER JOIN in SQL
✅ CROSS JOIN in SQL
✅ NATURAL JOIN in SQL
✅ SELF JOIN in SQL
✅ CROSS JOIN in SQL
✅ NATURAL JOIN in SQL
✅ SELF JOIN in SQL
👨💻 10 different ways to remove duplicate records in SQL
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1 - Delete using Unique Identifier.
2 - Using SELF join.
3 - Using Window function.
4 - Using MIN function. This delete even multiple duplicate records.
2 - Using SELF join.
3 - Using Window function.
4 - Using MIN function. This delete even multiple duplicate records.
5 - Using backup table.
6 - Using backup table without dropping the original table.
7 - Delete using CTID.
8 - By creating a temporary unique id column.
6 - Using backup table without dropping the original table.
7 - Delete using CTID.
8 - By creating a temporary unique id column.
🚀 You can become a data analyst in 90 days without spending a cent! Yes, you heard that right.
With the right approach and dedication, anyone can become a #dataanalyst . Here are the steps to follow( Make sure the order is maintained)
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With the right approach and dedication, anyone can become a #dataanalyst . Here are the steps to follow( Make sure the order is maintained)
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🤔 ¿Qué es un algoritmo? ¿Cómo resolver problemas con algoritmos? (Aplica hasta para la vida, pero lo estoy estudiando para programación jeje)
🧵 👇
🧵 👇
👉 Se denomina algoritmo a un grupo finito de operaciones organizadas de manera lógica y ordenada que permite solucionar un determinado problema. Una serie de instrucciones o reglas establecidas que, por medio de una sucesión de pasos, permiten arrojar a un resultado o solución.
⚙️ ¿Cómo construir un algoritmo?
Lo primero es: Tener claro el requerimiento
💡 Ejemplo: "Quiero encender la luz de la habitación, donde estoy en este momento"
👣 Lo que haremos....
Lo primero es: Tener claro el requerimiento
💡 Ejemplo: "Quiero encender la luz de la habitación, donde estoy en este momento"
👣 Lo que haremos....
Learn SQL projects for Data Analysis and add to your portfolio for free 🥳
Projects for the portfolio are very important. These projects will improve your skills
A thread 🧵👇
Projects for the portfolio are very important. These projects will improve your skills
A thread 🧵👇
1.
2.
3 SQL Games that will take your SQL skills to the next level
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1. SQL Murder Mystery
A murder has happened in SQL city!
Can you use your SQL knowledge to find the killer?
mystery.knightlab.com
A murder has happened in SQL city!
Can you use your SQL knowledge to find the killer?
mystery.knightlab.com
SQL Windows Functions Explained like you are 5!
SQL windows are essential for a data analyst. You must learn them. Learn here in 2 min.
A thread..⬇️
SQL windows are essential for a data analyst. You must learn them. Learn here in 2 min.
A thread..⬇️
So what are Windows Functions: — Summarize a particular table over a particular column(thus creating a window) and calculate the result using different functions(mentioned above)
Functions are divided into two types: the ranking and value functions. The ranking functions are used to simply assign numbers to the existing rows according to some requirements, and value functions are used to copy values from other rows to other rows within the defined windows
1/ Web3’s impact on the🌎global economy is growing exponentially. Yet, there remains a *deficit* of Blockchain #DataAnalyst talent😢
& In comes our ‘Data Alchemist Boot-Camp’🧪that *PAYs* you $2,000💵to learn Web3 Analytics.
Unlock your path towards a Web3 Career🧵👇🏼
& In comes our ‘Data Alchemist Boot-Camp’🧪that *PAYs* you $2,000💵to learn Web3 Analytics.
Unlock your path towards a Web3 Career🧵👇🏼
2/➡️A 'Data Alchemist'🧪is a self-starter who is dedicated to personal growth & advancing Web3 forward!
We started this initiative b/c...
💙Education is the largest barrier to onboarding users to Web3
💙Training more Blockchain #DataAnalyst will create jobs & value for Web3
We started this initiative b/c...
💙Education is the largest barrier to onboarding users to Web3
💙Training more Blockchain #DataAnalyst will create jobs & value for Web3
3/ Covalent’s 4-week course will train participants in Web3 Analytics, including
✅Reach, Retention & Revenue (RRR) Analysis
✅Multi-Chain Analysis (across 42+ chains Covalent indexes)
✅Web3 #DataStorytelling📚
✅Web3 Data Labeling🏷️
✅Reach, Retention & Revenue (RRR) Analysis
✅Multi-Chain Analysis (across 42+ chains Covalent indexes)
✅Web3 #DataStorytelling📚
✅Web3 Data Labeling🏷️
Hello Data Analyst,
Let's discuss data models & benefits of having solid data models.
One of a data analyst's key responsibilities is building a strong data model.
The term "Data Model" refers to the process of arranging data into tables based on relationships & groups...
Let's discuss data models & benefits of having solid data models.
One of a data analyst's key responsibilities is building a strong data model.
The term "Data Model" refers to the process of arranging data into tables based on relationships & groups...
...to minimize duplication and maximize efficiency.
By performing this task properly, you contribute to making it simpler for people to comprehend your data, which will make it simpler for both you and them to create useful reports and dashboards.
By performing this task properly, you contribute to making it simpler for people to comprehend your data, which will make it simpler for both you and them to create useful reports and dashboards.
It is challenging to provide a set of guidelines for what constitutes a good data model because every piece of data is unique and is used in different ways.
A smaller data model is preferable because it will operate more quickly and be easier to use.
A smaller data model is preferable because it will operate more quickly and be easier to use.
As a #DataAnalyst, your best north star for skills development is... speed! 🧵
Don’t be fooled by a simplistic interpretation of speed. A sloppy analyst who keeps falling for shiny nonsense “insights” will only slow everyone down in the long run. "Speed" means something nuanced. 🧵
Analysts must master many different forms of speed, including:
Speed of getting data that’s promising and relevant. (Domain knowledge.)
Speed of getting data ready for manipulation. (Software skills.)
Speed of getting data summarized. (Mathematical skills.)
...continued...🧵
Speed of getting data that’s promising and relevant. (Domain knowledge.)
Speed of getting data ready for manipulation. (Software skills.)
Speed of getting data summarized. (Mathematical skills.)
...continued...🧵
What's the difference between a Data Engineer , a Data Analyst & a Data Scientist?
If you want to make sure you don’t lose your job in the next five years, you probably want to know something about Big Data, or even switch to a data-related career.
#Thread
#DataScience #Data
If you want to make sure you don’t lose your job in the next five years, you probably want to know something about Big Data, or even switch to a data-related career.
#Thread
#DataScience #Data
You don’t need to be an excellent statistician or a high-class mathematician to work in data science or analytics, nor do you need a lot of prior programming knowledge.
- Dr. Rebecca Pope (Head, Data Science d Engineering at KPMG)
#DataScience #Data #DataAnalyst #Statistics
- Dr. Rebecca Pope (Head, Data Science d Engineering at KPMG)
#DataScience #Data #DataAnalyst #Statistics
However, you do need an interest in statistics, you do need to be willing to learn how to code, and you do need to know how to do some high level mathematical operations. Data scientists are not just statisticians.
#DataScience #Data #DataAnalyst #Statistics
#DataScience #Data #DataAnalyst #Statistics
What's the difference between a data engineer, a data analyst and a data scientist?
If you want to make sure you don’t lose your job in the next five years, you probably want to know something about Big Data, or even switch to a data-related career.
#Thread
#DataScience #Data
If you want to make sure you don’t lose your job in the next five years, you probably want to know something about Big Data, or even switch to a data-related career.
#Thread
#DataScience #Data
You don’t need to be an excellent statistician or a high-class mathematician to work in data science or analytics, nor do you need a lot of prior programming knowledge.
- Dr. Rebecca Pope (head of data science and engineering at KPMG)
#DataScience #Data #DataAnalyst #Statistics
- Dr. Rebecca Pope (head of data science and engineering at KPMG)
#DataScience #Data #DataAnalyst #Statistics
However, you do need an interest in statistics, you do need to be willing to learn how to code, and you do need to know how to do some high level mathematical operations. Data scientists are not just statisticians.
#DataScience #Data #DataAnalyst #Statistics
#DataScience #Data #DataAnalyst #Statistics
