Let's dive in and check them out one by one.

讓我們深入其中，逐一查看。

Being a data analyst is all about analyzing data to help make better business decisions.

數據分析師的工作就是分析數據，幫助做出更好的業務決策。

You'll need to get good at various skills from math and programming to data handling and visualization.

您需要掌握從數學和編程到數據處理和可視化的各種技能。

Let's jump in.

讓我們跳進去。

First up, you need a solid foundation in mathematics and statistics.

首先，您需要紮實的數學和統計學基礎。

This is crucial because data analysis relies heavily on these principles.

這一點至關重要，因為數據分析在很大程度上依賴於這些原則。

Focus on understanding basic concepts like mean, median, standard deviation, probability, and hypothesis testing.

重點理解平均數、中位數、標準差、概率和假設檢驗等基本概念。

Spend about a month or two getting comfortable with these topics.

花一兩個月的時間熟悉這些主題。

Next, you need to get really good at Excel.

接下來，你需要真正精通 Excel。

Excel is a powerful tool for data analysis and many companies still rely on it.

Excel 是一款功能強大的數據分析工具，許多公司仍在使用它。

Learn how to use functions, pivot tables and charts.

學習如何使用函數、數據透視表和圖表。

Spend about two to three weeks mastering Excel as it's a fundamental skill for any data analyst.

花大約兩到三週時間掌握 Excel，因為這是任何數據分析師的基本技能。

After Excel, you should get comfortable with SQL.

Excel 之後，您應該熟悉 SQL。

SQL stands for structured query language.

SQL 是結構化查詢語言的縮寫。

It's a simple language we use for managing and querying databases.

這是我們用來管理和查詢數據庫的一種簡單語言。

Learn how to write queries to access, organize, and analyze data.

學習如何編寫查詢來訪問、組織和分析數據。

SQL is pretty simple and you can get a decent grasp of it in about a month or two.

SQL 非常簡單，一兩個月就能掌握。

Next, you need to get the hang of Python.

接下來，您需要掌握 Python 的使用方法。

It's a versatile language that's widely used in data analysis.

這是一種廣泛應用於數據分析的通用語言。

Focus on learning the basics of Python, including libraries like Pandas and NumPy.

重點學習 Python 的基礎知識，包括 Pandas 和 NumPy 等庫。

You will also hear about R, that's another language used in data analysis.

您還將瞭解到 R 語言，這是另一種用於數據分析的語言。

However, if you're starting out, it's best to stick with Python first and think about learning R later.

不過，如果您是初學者，最好先學習 Python，然後再考慮學習 R。

Spend about a month or two getting the hang of Python.

花一兩個月時間掌握 Python 的使用方法。

You should also learn Git, that's a version control system we use to track changes to our code and collaborate with others.

您還應該學習 Git，這是一個版本控制系統，我們用它來跟蹤代碼的更改並與他人協作。

Git has a ton of features, but you don't need to learn all of them.

Git 有很多功能，但你並不需要全部學會。

Think of it like the 80/20 rule, 80% of the time you use 20% of Git's features.

就像 80/20 原則一樣，80% 的時間使用 20% 的 Git 功能。

So one to two weeks of practice is enough to get up and running.

是以，一到兩週的練習就足以讓你快速上手。

By the way, to help you on this journey, I've created a free supplementary PDF that breaks down the specific concepts you need to learn for each skill.

順便說一下，為了在學習過程中對你有所幫助，我製作了一份免費的補充 PDF 文件，其中對你需要學習的每項技能的具體概念進行了細分。

You can find the link in the description.

您可以在說明中找到鏈接。

Also, I have a bunch of tutorials on this channel and complete courses on my website if you're looking for structured learning.

此外，我在這個頻道上有很多教程，如果你想有系統地學習，我的網站上也有完整的課程。

Again, links are in the description.

同樣，鏈接也在說明中。

Next, focus on data collection and preparation.

接下來，重點是數據收集和準備。

This means gathering data from various sources and cleaning it up.

這意味著要從各種來源收集數據並進行清理。

So it's ready for analysis.

這樣就可以進行分析了。

Learn how to use Python libraries like Pandas to manipulate and clean data.

學習如何使用 Pandas 等 Python 庫來處理和清理數據。

Spend about a month or two on this.

花上一兩個月的時間。

Once your data is clean, you need to visualize it to spot patterns and communicate results.

數據清理完畢後，您需要將其可視化，以便發現模式並交流結果。

Learn how to use Python libraries like Matplotlib and Seaborn.

學習如何使用 Matplotlib 和 Seaborn 等 Python 庫。

Also, check out business intelligence tools like Tableau or Power BI.

此外，還可以查看 Tableau 或 Power BI 等商業智能工具。

They are widely used for creating interactive and shareable dashboards.

它們被廣泛用於創建交互式和可共享的儀表盤。

Power BI is especially cool because it's getting more popular and since it's a Microsoft product, it works great with other Microsoft tools you might be using.

Power BI 特別酷，因為它越來越受歡迎，而且因為它是微軟的產品，所以能與你可能正在使用的其他微軟工具完美配合。

Spend about a month or two on data visualization.

花一兩個月的時間學習數據可視化。

Now, while not essential for every data analyst role, having a basic understanding of machine learning can be a plus.

現在，雖然不是每個數據分析師職位都必須具備的條件，但具備對機器學習的基本瞭解可能會是一個加分項。

Machine learning involves teaching computers to make predictions based on data.

機器學習包括教會計算機根據數據進行預測。

If you're interested, spend a month or two learning the basics of machine learning, including Python libraries like TensorFlow and Scikit-learn.

如果你感興趣，可以花一兩個月時間學習機器學習的基礎知識，包括 TensorFlow 和 Scikit-learn 等 Python 庫。

Now, as you advance, you might encounter situations where you need to work with massive data sets.

現在，隨著時間的推移，您可能會遇到需要處理海量數據集的情況。

That's where big data comes in.

這就是大數據的作用所在。

Big data is all about handling and processing huge amounts of data quickly.

大數據就是要快速處理海量數據。

Tools like Hadoop and Spark are super handy for this.

Hadoop 和 Spark 等工具在這方面非常方便。

Spend a month or two getting familiar with these tools.

花一兩個月的時間熟悉這些工具。

So if you dedicate three to five hours every day, you can follow this roadmap and pick up all the skills you need to apply for an entry level data analyst job in about 8 to 16 months.

是以，如果你每天投入三到五個小時，就可以按照這個路線圖，在大約 8 到 16 個月內掌握申請初級數據分析師工作所需的所有技能。

If you have any questions, please let me know in the comments below and I'll do my best to answer you right here or in my future videos.

如果您有任何問題，請在下面的評論中告訴我，我會盡我所能在這裡或今後的視頻中回答您。

If you enjoyed this video, please give it a like and subscribe for more useful content.

如果您喜歡這段視頻，請點贊並訂閱，以獲取更多有用的內容。

Thanks for watching.

感謝觀看。