Mokami

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Mokami

Mokami

@Just_Mokami

In God we trust, everyone else must bring data.

Nairobi, Kenya Katılım Aralık 2019
1.9K Takip Edilen214 Takipçiler
Mokami
Mokami@Just_Mokami·
@thenaijacarguy Hi Gabby. Could you please share the data? I'd love to replicate it.
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Gabby
Gabby@thenaijacarguy·
What’s the best tool to replicate this dashboard?
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Johnnie
Johnnie@_onsase·
@_fels1 Meditations By Aurelius
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Ja Loka
Ja Loka@_fels1·
Name one book you have read this year that you'd recommend every man to read before the end of this year.
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Starlink
Starlink@Starlink·
No long-term contracts. Easy to order online
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J.A Olaoye
J.A Olaoye@JA_Olaoye·
Data Analyst share your portfolio or screenshot of visualization you developed. I want to see something. Don’t be shy. Share your work. The globe is watching.
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Mokami
Mokami@Just_Mokami·
@blue_resolve @iam_Uchenna I am not defending anything, just stating the fact that this is an opinion. He mentions "Somebody said..." The statistics is correct, the validity of the data or hypothesis is up for discussion.
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Briareos Hecatonchires
Briareos Hecatonchires@blue_resolve·
@Just_Mokami @iam_Uchenna 'if'? 'if'? Really? We now use counterfactuals to defend invalid/heavily flawed positions? IQ (whatever that means, with all its flaws) says nothing about one's ability to say ANYTHING sensible.
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Splendor of SQL 🇬🇧🇩🇪💖
Someone said “The median Nigerian man is daft with nothing sensible to say.” Looking at this from a data analyst perspective: In a perfectly symmetrical distribution, the mean and the median would be the same. However, in real-world data, such as the heights, weights, incomes, or other characteristics of Nigerian men, the distribution is often skewed, making the median and mean different. What do you think @starboy_abefe
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Mokami
Mokami@Just_Mokami·
@blue_resolve @iam_Uchenna If he is basing his assertions or opinion on a numerical variable like IQ score (which could be aggregated), a normal distribution would give mean = median = mode. Either sides of the distribution would be symmetrical.
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Briareos Hecatonchires
Briareos Hecatonchires@blue_resolve·
@iam_Uchenna There's no such thing as median for categorical variables. I think the sentiment here is the average (which originally covers mean, median & mode) man. In this case 'mode' is what is correct. Mode = most occurring Nigerian male behaviour (if behaviours are categorized).
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Ezekiel
Ezekiel@ezekiel_aleke·
Dear Data Analyst! Here is the order of writing SQL code: 1. SELECT 2. TOP 3. FROM 4. WHERE 5. JOIN 6. GROUP BY 7. HAVING 8. ORDER BY Learn and repost❤️
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Mokami
Mokami@Just_Mokami·
@deekshas24 I made this little query that might also be helpful understanding the order of execution.
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Deeksha
Deeksha@deekshas24·
Day 2 of 21 #SQL Challenge: Understanding the execution order of an SQL query: 1. FROM 2. WHERE 3. GROUP BY 4. HAVING 5. SELECT 6. ORDER BY 7. LIMIT Mastering this order is key to writing efficient SQL queries! #SQL #Database #LearningSQL #21DayChallenge
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Mokami retweetledi
Sasi 📊📈
Sasi 📊📈@freest_man·
In SQL, the "window" in "window function" refers to a set of rows. Window functions are a powerful tool in SQL that allows you to perform calculations over a set of rows, called a window, that is related to the current row. In SQL, window functions are used to perform calculations on a set of rows and return multiple rows for each group They are distinguished from other SQL functions by the presence of an OVER clause. Window functions are similar to aggregate functions, but they do not collapse the result of the rows into a single value. Instead, all the rows maintain their original identity, and the calculated result is returned for every row. Some common use cases for window functions include calculating running totals, rankings, and moving averages. To use a window function, you need to specify the function name followed by an OVER() clause. The OVER() clause defines the window of rows that the function will operate on. For example, to calculate a running total of sales by date, you could use the SUM() function with an OVER() clause like this: SELECT date, SUM(sales) OVER (ORDER BY date) FROM sales_table; The ORDER BY clause within the OVER() clause specifies the order in which the rows should be processed. In this case, we’re ordering by date so that we get a running total of sales by date. You can also use PARTITION BY within the OVER() clause to divide the rows into partitions to which the window function will be applied. For example, to calculate a running total of sales by date and store, you could use: SELECT date, store, SUM(sales) OVER (PARTITION BY store ORDER BY date) FROM sales_table; Another useful window function is RANK(). This function assigns a rank to each row within the window based on the values in one or more columns. For example, to rank sales by store and date, you could use: There are several other ranking functions available in SQL, including DENSE_RANK(), ROW_NUMBER(), NTILE(). Each of these functions assigns a rank or number to each row within the window based on slightly different criteria.
Sasi 📊📈 tweet mediaSasi 📊📈 tweet mediaSasi 📊📈 tweet mediaSasi 📊📈 tweet media
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Gbóláhàn Adébáyò
Gbóláhàn Adébáyò@gbolahaann·
Saw a blog post on building The economist style charts with Tableau sometime last week and I’ve been looking for it since then. Anyone share this post if they find it? Thanks!
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David Clinton
David Clinton@daveclintonn·
27 incredible years 🎉 Looking forward to more unforgettable adventures and experiences. Cheers to my birthday!
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Mokami
Mokami@Just_Mokami·
@iam_Uchenna MySQL: SELECT users.id AS id, users.name AS name, SUM(orders.price) AS total_purchases FROM users JOIN orders ON users.id = orders.user_id GROUP BY 1,2 ORDER BY 3 DESC LIMIT 10;
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Mokami
Mokami@Just_Mokami·
@iam_Uchenna There's no hurry in Africa, bro. 🙂
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Mokami
Mokami@Just_Mokami·
Choosing the right visualization is crucial for effective data communication. It allows your audience to draw insights, and make informed decisions. Considering the context, which visualization would resonate most with you?  Viz: tabsoft.co/3U7pG0k
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Mokami
Mokami@Just_Mokami·
@im_akeelah I like you explored different angles of the data (age & gender). What is the name of the chart you used to compare gender?
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