Introduction
During my first week of learning Data Science and Analytics, I was introduced to Microsoft Excel and how it can be used when working with data. I learned that Excel can also be useful for data analytics because it provides different tools for organizing, checking, cleaning, and analyzing data.
Also I learned is that data needs to be prepared before it can be analyzed properly. When we receive raw data, it may contain missing information, duplicate records, spelling mistakes, extra spaces, or values that are not in the correct format. If these problems are not corrected, they can affect the results of the analysis.
These include the basic structure of Excel, entering and organizing data, formulas and functions, sorting and filtering, and some basic data-cleaning techniques and its importances on data sciences and analytics;
1. Excel structure
An Excel file is called a workbook, and inside a workbook there can be one or more worksheets. A worksheet is where the actual data is entered. I noticed that the worksheet is made up of rows and columns. The columns are labeled with letters such as A, B, C, and D, while the rows are labeled with numbers such as 1, 2, 3, and 4.
Also, Where a row and column meet, there is a cell. Each cell has a reference, such as A1 or B5. I learned that these cell references are important because they allow me to use the contents of one cell in a formula.
For example, if I have a quantity in cell B2 and a price in cell C2, I can use the formula:
=B2*C2 this is to calculate the total.
This was one of the first things that helped me understand that Excel can be used for more than just storing information.
2. Understanding columns and rows
I also learned how rows and columns are normally used when working with a dataset. A column usually represents a variable or a type of information, while a row represents one record or observation.
I learned that keeping this structure is important when preparing data for analysis. If the information is not organized properly, it becomes more difficult to perform calculations or find information.
3. Different Types of Data
I also learned that Excel can work with different types of data. The main types I came across were text, numbers, and dates. This showed me that data preparation is not just about how the spreadsheet looks. It is also about making sure Excel understands the information correctly.
4. Using Formulas
A formula tells Excel to perform a calculation. Formulas normally start with an equals sign (=).
For example, if I have a quantity in B2 and a price in C2, I can calculate the total using:
=B2*C2
This is useful because Excel can do the calculation for me instead of requiring me to calculate it manually. I also learned that formulas can be copied down to other rows. This is useful when working with a dataset containing many records.
5.Basic Functions
some basic Excel functions. Functions are built-in commands that perform common calculations.
Some of the functions I learned include;
SUM, AVERAGE, COUNT, MIN, and MAX.
SUM -This function adds numbers together
AVERAGE - This function calculates the average of a group of numbers.
COUNT-This function counts cells that contain numbers and can tell me how many numerical entries are in the selected range.
MIN and MAX- MIN finds the smallest number, while MAX finds the largest number.
6. Sorting data
Sorting means arranging data in a particular order. For example, I can sort a list of sales from the smallest amount to the largest amount. I can also sort it from the largest amount to the smallest. With that I can also sort names alphabetically.
7. filtering data
Filtering allows me to display only the records that meet a particular condition. For example, if I have a dataset containing customers from different locations, I can filter the Location column to display only customers from Nairobi.
8. Data cleaning
One of the most important things I learned during Week 1 was the idea of data cleaning.
Data cleaning means checking raw data for errors and making corrections so that the data is more suitable for analysis.
When data is collected, it is not always perfect. There may be mistakes because people entered the information manually, because different systems were used, or because information was collected from different sources.
Some common problems include:
-Duplicate records
-Missing values
-Spelling mistakes
-Extra spaces
-Different capitalization
-Incorrect formats
Top comments (0)