Hello, this is the very first time I would be showing my data science journey online. It took a lot of courage to write this. I have a journal where I write everything I learn daily with Udemy but this time around, it is different. A friend told me the importance of showing my work and she also recommended a book to read. So, this is like a compilation of my everyday journey with the course. Now, that you are here. Welcome!

The complete data analyst course was gifted to me and I love how explanatory the course is.

Day 01 of Data Analytics with Udemy: I got introduced to the world of business and Data. I learned relevant terms like Business, Business Analytics, Business Intelligence, Data Analysis, Data Analytics, and Data Science. I learned the Quantitative and Technical skills required. I also got introduced to the data analyst Job description and how data itself isn’t a valuable asset, the insights are the real treasure.

Data Gathering/Collection → Data Cleaning → Data Pre-processing →Visualization& Analysis.

Day 02 of Data Analytics with Udemy: After the introduction to data analytics class, I learned how to set up the Jupyter Notebook. The next class which I enjoyed so much because of how explanatory it was. We started it with “Why python?”, I also got introduced to the various Python Libraries. Pandas(Data Cleaning), Numpy(Pre-processing), Matplotlib & seaborn(Visualization). I got to know that python is an open-source, general-purpose high-level programming language. It is also case-sensitive.

Starting with Python basics, I learned about variables and how they are used to store information. I learned the different types of Data(Boolean, Strings, and Numbers(Floats and Integers)).

Day 03 of Data Analytics with Udemy: Today’s class was on Basic Arithmetic operations in python. I got to learn the difference between an operator and an operand. I worked with %,+,> =, < =,!=,==,*,/.I learned how to reassign values to variables, make comments with(#), Line continuation with(\), and also Indexing in python. The next class was on Logical and Identity Operator in python and it was quite interesting :). I learned about “and”, “not”, “or”. I also learned Conditional Statements where I learned how to work with IF, ELSE IF(ELIF), and ELSE.

Day 04 of Data Analytics with Udemy: Today, I learned functions and also how to use a function in another function. I got introduced to the Notable Built-in functions in python. I practiced Lists, Indexing, Slicing lists, Tuples, and Dictionaries. Now, I know when to use Curly braces and square brackets. I also learned the For loop, While loop, and Incrementing for iteration. I learned how to use Conditional Statements and Loops together. Day 04 was a bit challenging because I had to watch and take the classes again.

Day 05 of Data Analytics with Udemy: This class was on Fundamentals For Coding in python. I learned cool stuff like OOP (Object-oriented Programming) and how every value in python is an object, what differentiates a class, object, and attribute. I learned about Modules, Packages, and python Standard Libraries. Today became more interesting as I got introduced to NumPy. I learned that Numpy stands for Numeric Pythons and its core is .ndarray object. It deals with Scalars, Vectors, and Matrices.

Day 06 of Data Analytics with Udemy: Today, I got to know about software documentation and how it is a collection of data stored as written texts. I got to know about Python Documentation. The next class was on MATHEMATICS WITH PYTHON. I learned about Linear Algebra and how to work with Matrices using Numpy. Addition and Subtraction of Matrices and also the Scalar addition to matrices.

Day 07 of Data Analytics with Udemy: Still on Mathematics with Python I got to learn the difference between Scalars, Vectors, and Matrices. Scalars with zero dimension, Vectors with One dimension, and Matrices being a collection of vectors. I learned arrays in python. I know how to transpose a matrix. I learned the dot product of matrices. I also got to know why linear algebra is useful. I learned the Numpy basics, the Numpy package, why we use it, and what a package is all about.

Day 08 of Data Analytics with Udemy: Today, I got introduced to PANDAS(Panel Data). I got to know about the Pandas Library and I learned that Pandas is built on Numpy. I learned that a Data frame is a collection of series. I learned the Importance of Pandas from how it makes data analysis with python easier and faster to how it helps to store datasets containing multiple types of information(numbers, strings, Boolean, etc). I also got to know that the Pandas library can import data from different file formats. I learned how to install, upgrade and import Pandas and also how to work with the pd.series. The next class was “Working with Attributes in Python” and I learned that almost every entity in python is an object.

thank you so much for reaching this far!

Day 09 of Data Analytics with Udemy: Today, I got to learn the difference between pandas series and pandas data frame. I learned how to use the.size,,dtype ,and.sum attributes. I worked with examples on pd.series and how to make a series from a list and dictionary. I also learned about pandas Data frames and how to construct a data frame from a dictionary of lists. I also go to know about the errors that can be made and also how all arrays must be of the same length. I learned how to specify an index with pd.Dataframe. I got to know about df.columns,df.index, and df.shape. I also learned how to construct a Dataframe in a professional way.

Day 10 of Data Analytics with Udemy: I worked on the exercises provided and I got to learn the difference between Labelled-based indexing and position-based indexing in python. Range index(start value, stop value, step value). I also learned about using methods in python. I learned the difference between functions and methods. Different Libraries contain their own types of methods. Methods like.sum(), .min(), .max(),.head(),.tail().

Day 11 of Data Analytics with Udemy: Today, I learned the difference between a parameter and an argument. The next class was on Pandas Documentation(and it was pretty long). I got to see how the pandas worse and the required skills to become a Pandas independent user.

Day 12 of Data Analytics with Udemy: Today’s class was on Introduction to Pandas DataFrames.A data frame has both rows and columns I got introduced to the Pandas DataFrames object and its purpose. I learned the difference between Parameters and Arguments. The next class was on “Creating Dataframes from scratch 1”. I learned 6 different ways of creating Dataframes. (Creating from a dictionary of lists, from a list of lists +specying and index, from a list of dictionaries). From there, I moved to “Creating Dataframes from scratch 2” and I learned that the shape attributes help in telling how many rows and columns are in your data frame.

Day 13 of Data Analytics with Udemy: Today, I learned “to work with text files in python”. A file is any item that can be created, modified, stored, or deleted by the user or the operating system. There are sound, video, and text files. Python only maintains two categories of files;Text files and Binary files. I learned about EOL and I got to know that Text files and Text data are two different terms that must not be confused. In the next class, I learned the differences between File and File Objects and also the differences between reading and Parsing(syntactic Analysis).

Day 14 of Data Analytics with Udemy: Today, I learned about Structured Vs Semi-structured and Unstructured data. I got to know that Structured data is also Traditional data and it is also in a tabular form. Structured data can be stored in databases and it’s based on using relational databases.Excel, SQL, and Pandas DataFrame. Semi-Structured data on the other hand use different patterns for easy analysis. Unstructured data is organized in such a way that makes finding a specific piece of it, hard. Video, audio, photos, and web pages are all examples.

Day 15 of Data Analytics with Udemy: Today, I got introduced to BIG DATA. I learned about Data Connectivity through text files and the most widely used test file formats are .json, .xml,.csv,.txt, and .xlsx. I would be focusing on .csv, .json, .xlsx. The .csv is perfect for storing tabular data and all modern programming languages can read, export to, and work on .json. I also learned the difference between a plain text file and a rich text file.

Watch out for the next part.

Thank you so much for reading!!!

You can connect with me;

Linkedin- www.linkedin.com/in/barakahshittu

Twitter-https://twitter.com/Barakah_sh