Why this course
Save your time and learn only what you will need 99% of the time in a real workplace.
Python and Data Science are infinite … you can keep learning and learning forever. But in the end, in real life, you will need to perform, as quickly as possible and almost always you will need just a few commands and skills. And this comes down to knowing the skills, the techniques, taught in this course!
Many people learn, learn, learn, in order to feel confident and proud, but unfortunately, they will forget most of what they learnt, simply because they will never use it!
The idea of this course is not to learn anything by heart. Because almost all that you will need is in here. When you don’t remember how something is done in Python, you just come to this course and search for the keyword and immediately watch the video and remind yourself.
The code is updated every 2-3 months! Come here every few months and download the new code!
The course includes more than 150 lectures. Each lecture describes one important command, with examples. Everything is updated regularly! Otherwise, it would stop running. So updates are vital!
Employment:
This course is not only for Data Scientists. This course is for everyone actually, because one way or another it may happen that you will need to code in Python. Or you may be asked to read Python code and understand it!
What makes this course so different from others:
You save time because you learn the code that is truly needed at the workplace. In other words, it is 100% sure that you will encounter this code! You will not learn any unnecessary theories or abstract mathematics. You will learn only what is truly needed!
Visit www realdatamodels com and at the bottom right corner send any questions you may have to the chat! It goes directly to the mobile phone of the Instructors and you will get a reply within a day! No payment. No subscription is required. Nothing hidden or sneaky.
You can connect with the instructors live on a chat at www realdatamodels com ! No hiding behind excuses of the type “I am busy”. The Instructors are available always! Just read the comments! We help students for free! (because this helps us attract more students and we want to grow!)
Get Instant Notification of New Courses on our
Telegram channel.
See how many students have enrolled in this course! Read the reviews! We help our students! We want you to come back and buy all of our courses!
The course is suitable for you even if you have no background in Data Science or Python.
Actually, no background is required!
- If you think that Data Science coding is complicated, it’s not.
- If you think watching this course won’t make a difference in your professional career … think again. Remember: This course will increase your efficiency because you will have all the commands you need, in one place! This course is like an Encyclopedia/library for you!
- If you think that you will get stuck because you do not have experience or knowledge: NO you will NOT get stuck because…. www realdatamodels com : Send your questions at the chat at the bottom right corner of our website.
Enrol today and enjoy:
- Lifetime access to the course and all future updates that the course will receive (the code and the videos are regularly updated).
- High-quality, up-to-date video lectures. With subtitles that are carefully written and accurate.
- Instructors who TRULY care and reply as fast as possible! You can connect with the Instructors and discuss in real-time on the website www realdatamodels com. Literally, real-time: you send a message, we read it on our phone and reply!
- A 30-day, no questions asked, money-back guarantee.
- Fully responsive feedback: If you do not like something, we are going to record it again or make a new video with more explanations!
With the 30-day 100% money-back guarantee, there’s no reason to hesitate.
We will give you 3 more reasons why you should enrol:
- Your investment in this course will quickly be paid back. We live in a period where Data Science is among the most popular topics. Knowing the most important commands will set you apart!
- You need to start as soon as you can (like really right now) and learn the most important commands really well! This will unlock your creativity and boost your confidence!
- The best way to remember and absorb information is to learn something that you know you will need for sure!
I CAN’T WAIT TO SEE YOUR SUCCESS
See you inside!
Who:
We are a team of Researchers in Data Science Projects between Academia and Industry.
To connect with us visit: www realdatamodels com
The entire Research Team is there!
Introduction
Introduction
Installing the software
Installing Python
Interacting with data from external sources
Read an .xlsx file
How to skip reading some rows when reading an excel file
How to read a specific sheet from an excel file into a dataframe
How to set the index of a dataframe while reading it from the excel file
How to read specific columns from an excel file into a dataframe (usecols)
How to read data from World Bank’s online database
How to send many dataframes into the same excel file (xlsx) in different sheets
Sending a dataframe to a csv file
How to hide warnings that Python produces. And how to trigger manually warnings
How to read only some rows from the top/bottom of a dataframe(nrows, skipfooter)
How to check if an Excel cell is empty
How to see the version of the packages we have installed
How to hide special warnings
Index of a dataframe
Columns becoming the index and vice versa: reset_index, set_index, drop=T
How to change the index name of a dataframe
How to find the row index & column index of any element of a dataframe
How to enumerate the rows (enumerate) and use it in for loops
How to sort the index of a dataframe (sort_index)
Lists
How to sort the elements of a list using lambda functions
How to remove some elements from a list at once
How to create a list (sublist) that has some elements of another list
Defining a list with numbers 1,2,3,..,9 using list comprehension
How to print the first 5 and the last 5 elements of a list
How to include the elements of another list into a list (extend versus append)
How to remove all occurrences of an element from a list
Difference between pop() and remove()
List comprehension
Slicing
Enumerate the list (enumerate, index)
The command isin: series.isin(list)
Count how many times an element is in a list (count)
How to create a copy and an alias of a list
Dataframes at workplace
How to return elements from a dataframe
How to delete rows/columns from a dataframe using iloc, drop
How to read the row /column index and values (df.values)
How to show the max number of rows and columns of a dataframe (set_option)
How to create a copy of a dataframe
How to correctly take a backup of a dataframe (copy() vs =)
How to change specific values of a dataframe while leaving the rest unchanged
Create a new column and populate with elements of another column of a dataframe
How to change the order of the columns of a dataframe
How to create a new row in a dataframe and fill it with values from other rows
How to fill a new column with values 1,2,3,… (np.arange)
How to use Pivot tables on Python
How to rename rows and columns of a dataframe
How to create a dataframe using a dictionary
How to find the transpose of a dataframe
Selecting rows and columns from a dataframe
Copy-paste a row of a dataframe (np.repeat)
How to sort the columns of a dataframe
How to change the data type of a column / row of a dataframe
How to select many rows (loc, arange)
How to delete many rows from a dataframe at once
How to return the value under other columns, in the same row of a dataframe
How to Iterate through the rows of a dataframe iteritems
How to sort the values of a column (sort_values() )
How to populate a column using list, arrays, Series
Define a dataframe with and without a dictionary
Define a dataframe using list comprehension
The command “Apply”
Format the values of a dataframe to percentages (apply)
Format the values of a dataframe to show 1 decimal point
Apply a function to every element of a series/ dataframe (apply)
Loops
Prevent copying rows from a dataframe to dict (continue)
How to not allow duplicate values while inserting a new row (next)
Avoid duplicate entries using while and break
continue, break, pass
for – else
while for equivalence
While True
While else
multi – level (column) dataframes
How to define a dataframe whose column index has many levels (headers)
How to rename a column (rename)
How to remove a level
Compressing all levels into 1 excel cell or showing them as is (merge_cells)
How to print dataframe merged cells as unmerged in excel (startrow)
How to iterate through the rows of a multi level dataframe (iteritems)
Conditionals
if- elif statement
Inline if statement
Print statement with If Else included
Logicals
How to correctly write AND OR TRUE FALSE
How to correctly write the NOT operator
The De Morgan’s Law. AND, OR, NOT, statements
Comparing objects of type int, str, float, bool with each other
Type conversions: Int, Float, Str, Bool
Combining NOT with empty lists and strings
what it means for x to be none, empty list, empty string
Tuples
How to iterate through tuples. Different types of for-loops
How to concatenate 2 tuples
Defining a tuple
Sorting a tuple
Enumerating a tuple (enumerate, index)
Find the frequency of elements in a tuple (count)
Tuple immutability
NaN values
How to remove NaN values by deleting rows or columns (dropna)
Find if a dataframe has at least 1 missing value. And find their exact location!
Using min_count to sum if there are NaN values
Manually place NaN values to dataframes
Sum rows of a dataframe by ignoring NaN (skipna)
Replace missing values with 0
Python Implementation of Excel Functions
Model the Vlookup Excel function on Python (map function)
Model the SUMIFS function on Python
Model the AVERAGEIFS function on Python
Strings
How to evaluate string expressions using eval ()
Removing characters from end, start of a string (lstrip, rstrip)
How to break a long sequence of characters in sets of characters (wrap)
How to select part of a string (e.g. all string except last 3 characters)
Use replace() to remove white spaces from a string.
Find multiple occurrences of a subtext in a long string
Selecting specific characters from a column using “str”
How to replace text of a string using replace()
Join strings from inside a list (join)
multiline strings
formatting strings and f-strings (format)
Count how many times a character is in a string
in, find() with strings
Right justify text (rjust)
Creating variables
How to define variables using globals()
Multiple assignment
Sets
Define a set, add/remove elements
Convert a list and a string to a set
Difference of two sets. Symmetric difference. Difference update
Set comprehension
Subset, superset, proper subset
Intersection & union of two sets
Series
Editing strings inside series (series.str[])
How to define a series object that has a constant value (pd.Series)
Selecting a column as a Series object versus as a Dataframe
Storing an array to a dataframe (broadcasting)
Numpy Arrays
How to concatenate arrays (append)
How to create equally spaced numbers linspace
Reshaping the arrays (reshape)
1D 2D 3D arrays from lists
How to modify elements of an array
How to use arange to create 1D and 2D arrays
eye ones zeros. Instantly make arrays of constants
Flattening an array (collapsing it to 1D) (flatten() )
Functions
Docstring
Count how many times a function is called
How to return many values from a function
Default values for parameters
A function calling another function
The global statement and global variables. Frequent errors
Dates
How to update a value in a DateTime index in a dataframe.
Using the Workalendar package for country-specific Dates
Use timedelta() for time conversions
Datatypes
Use __name__ to find the datatype of an object
How to check if the datatype of a variable is: int, float, str, NaN, Nonetype
Datatype of every element of a dataframe: for loop, dtypes, astype()
Converting the datatype of the elements of a column (astype)
Dictionaries
Define a dictionary and loop through it
How to find the number of elements in a dictionary (len)
Convert a dictionary into a list/set of keys/values. Find its unique values
How to convert a dataframe to a dictionary (to_dict) and how to use it
How to print the first 6 elements of a dictionary
What it means to check if x is in dictionary
How to convert a single value into a dictionary (all keys are this value)
How to avoid errors when a key is not found in a dictionary (command: get)
How to unite two dictionaries (double asterisk)
Dictionary comprehension
Delete a key from a dictionary
Sort a dictionary
Mutability of a dictionary
Special types of dictionaries
Default dictionary: how it works and why use it.
Basic mathematics
Trigonometry , infinite and pi
regular/integer/modulo division // %
dot product of two arrays np.dot
Errors (Exceptions)
Deal with errors via the Try-Except block
The statement “finally”
How to manually trigger errors based on user input (raise ValueError)
Trigger errors manually ( raise error)
Random package
random choice
randint
randrange
random.random, random.seed
random.sample (sample without replacement)
function for fixing random seeds
