Data Science Code That Appears All The Time At Workplace.

Learn exactly all the Programming (Python) skills that are needed all the time at workplace. Each video = 1 skill.
What you'll learn
In practice, 1% of Python is used 99% of the time. This course focuses on this 1%.
Great for Quants/ Economists, Data Scientists/ Software Engineers: the skills shown here, come up all the time.
This is your Help Resource when you are under heavy pressure!
You will not need to google-search to find answers again. Everything you need is in this course.
The subtitles are manually created. Therefore, they are fully accurate. They are not auto-generated.
Part of the giannelos dot com official certificate for high-tech projects.
Requirements
The only prerequisite is to take the first course of the "giannelos dot com" program , which is the course "Data Science Code that appears all the time at workplace".
Description
What is the course аbout:This online course focuses on the part of Data Science that keeps appearing all the time in any workplace. Save time and learn only what you will need 99.9% of the time. The idea is that this course can be your encyclopedia. When you don't remember how something is done on Python, you just resort to this course. You might have realized that Python and Data Science are like an ocean; you can keep learning and learning, but in the end, at work, you will need to perform, as quickly as possible. And this comes down to knowing the skills, the techniques, taught in this course! ​There are no prerequisites.Every topic is analyzed in depth so you can feel confident about what you learn.Every video is a building block. Once you know these building blocks you can do anything with data science. This course corresponds to the official certificate for the famous giannelos dot com program for high-tech projects. Who:I am a research fellow at Imperial College London, and I have been part of high-tech projects at the intersection of Academia & Industry for over 10 years, prior to, during & after my Ph.D. I am also the founder of the giannelos dot com program in data science.Doctor of Philosophy (Ph.D.) in Analytics & Mathematical Optimization applied to Energy Investments, from Imperial College London, and Masters of Engineering (M. Eng.) in Power Systems and Economics. Important:Prerequisites: The course Data Science Code that appears all the time at Workplace.Every detail is explained, so that you won't have to search online, or guess. In the end, you will feel confident in your knowledge and skills. We start from scratch so that you do not need to have done any preparatory work in advance at all. Just follow what is shown on screen, because we go slowly and explain everything in detail.
Overview
Section 1: Introduction
Lecture 1 Overview
Lecture 2 Full analysis
Section 2: Installing the necessary software
Lecture 3 Install Python
Section 3: Interacting with data in external sources
Lecture 4 How to read an xlsx file
Lecture 5 How to skip reading some rows when reading a dataframe
Lecture 6 How to read a specific sheet from an excel file into a dataframe
Lecture 7 How to set the index of a dataframe upon reading it
Lecture 8 How to read specific columns from an excel file into a dataframe (usecols)
Lecture 9 How to read data from World Bank's online database
Lecture 10 How to send many dataframes into the same excel file (xlsx) in different sheets
Lecture 11 Sending a dataframe to a csv file
Lecture 12 How to hide warnings that Python produces. And how to trigger manually warnings
Lecture 13 How to read only some rows from the top/bottom of a dataframe(nrows, skipfooter)
Lecture 14 How to check if an Excel cell is empty
Lecture 15 How to see the version of the packages we have installed
Section 4: Index of a dataframe
Lecture 16 Columns and index: reset_index, set_index, drop=T
Lecture 17 How to change the index name of a dataframe
Lecture 18 How to find the row index & column index of any element of a dataframe
Lecture 19 How to enumerate the rows (enumerate) and use it in for loops
Section 5: Lists
Lecture 20 How to sort the elements of a list
Lecture 21 How to remove some elements from a list at once
Lecture 22 How to create a list (sublist) that has some elements of another list
Lecture 23 Defining a list with numbers 1,2,3,..,9 using list comprehension
Lecture 24 How to print the first 5 and the last 5 elements of a list
Lecture 25 How to include the elements of another list into a list (extend versus append)
Lecture 26 How to remove all occurrences of an element from a list
Lecture 27 Difference between pop() and remove()
Section 6: Dataframe HOW-TOs
Lecture 28 How to return elements from a dataframe
Lecture 29 How to delete rows/columns from a dataframe using iloc, drop
Lecture 30 How to read the row /column index and values (df.values)
Lecture 31 How to show the max number of rows and columns of a dataframe
Lecture 32 How to create a copy of a dataframe
Lecture 33 How to change specific values of a dataframe while leaving the rest unchanged
Lecture 34 Create a new column and populate with elements of another column of a dataframe
Lecture 35 How to change the order of the columns of a dataframe
Lecture 36 How to create a new row in a dataframe and fill it with values from other rows
Lecture 37 How to fill a new column with values 1,2,3,... (np.arange)
Lecture 38 How to use Pivot tables on Python
Lecture 39 How to rename rows and columns of a dataframe
Lecture 40 Going through every element with loc, iloc and nested for loop
Lecture 41 Copy-paste a row of a dataframe (np.repeat)
Lecture 42 How to sort the columns of a dataframe
Lecture 43 How to change the data type of a column of a dataframe
Lecture 44 How to prevent reading a row of a dataframe using: iterrows, continue and a list
Lecture 45 How to select many rows (loc, arange)
Lecture 46 How to not allow duplicate values while inputting a new row in a dataframe
Lecture 47 How to return the value under other columns, in the same row of a dataframe
Lecture 48 How to Iterate through the rows of a dataframe iteritems
Lecture 49 How to delete many rows from a dataframe at once
Lecture 50 How to correctly take a backup of a dataframe (copy() vs =)
Section 7: multi - level (column) dataframes
Lecture 51 How to define a dataframe whose column index has many levels (headers)
Lecture 52 How to rename a column in a dataframe with many column index levels (rename)
Lecture 53 How to remove a level from a dataframe with many column levels
Lecture 54 Compressing all levels into 1 excel cell or showing them as is (merge_cells)
Lecture 55 How to print dataframe merged cells as unmerged in excel (startrow)
Lecture 56 How to iterate through the rows of a multi level dataframe (iteritems)
Section 8: Conditionals
Lecture 57 Inline if statement
Section 9: Logicals
Lecture 58 How to correctly write AND OR TRUE FALSE
Lecture 59 How to correctly write the NOT operator
Lecture 60 The De Morgan's Law. Many and, or, not statements
Lecture 61 Comparing objects of type int, str, float, bool with each other
Lecture 62 Clarify difference between is =
Section 10: Tuples
Lecture 63 How to iterate through tuples. Different types of for-loops
Section 11: NaN values
Lecture 64 How to remove NaN values by deleting rows or columns
Lecture 65 Find if a dataframe has at least 1 missing value. And find their exact location!
Lecture 66 Using min_count to sum based when there are NaN values
Lecture 67 Manually place NaN values to dataframes
Lecture 68 How to sum rows of a dataframe by ignoring or considering NaN (skipna, replace)
Section 12: Python Implementation of Excel Functions
Lecture 69 Model the Vlookup Excel function on Python (map function)
Lecture 70 Model the SUMIFS function on Python
Lecture 71 Model the AVERAGEIFS function on Python
Section 13: Strings
Lecture 72 How to evaluate string expressions using eval ()
Lecture 73 Removing trailing characters (rstrip)
Lecture 74 How to break a long sequence of characters in sets of 4 characters (wrap)
Lecture 75 How to select part of a string (e.g. all string except last 3 characters)
Lecture 76 Remove white space / blanks from a string (replace()
Lecture 77 How to search for a subtext across a list of strings
Lecture 78 Selecting specific characters from a column using "str"
Lecture 79 How to replace characters or words from inside a string
Section 14: Datatypes
Lecture 80 Use __name__ to find the datatype of an object
Lecture 81 Type conversions: Int, Float, Str, Bool
Lecture 82 Combining NOT with empty lists and strings
Lecture 83 How to check if the datatype of a variable is: int, float, str, NaN, Nonetype
Lecture 84 Datatype of every element of a dataframe: for loop, dtypes, astype()
Lecture 85 what it means for x to be none, empty list, empty string
Section 15: Creating variables
Lecture 86 How to define variables using globals
Section 16: Sets
Lecture 87 Comparing the index of two dataframes using sets
Section 17: Dictionaries
Lecture 88 How to find the number of elements in a dictionary (len)
Lecture 89 How to convert a dictionary into a list/set of keys/values.
Lecture 90 How to convert a dataframe to a dictionary (to_dict) and how to use it
Lecture 91 How to print the first 6 elements of a dictionary
Lecture 92 What it means to check if x is in dictionary
Lecture 93 Default dictionary (part 1)
Lecture 94 Default dictionary and lambdas
Lecture 95 How to convert a single value into a dictionary (all keys are this value)
Lecture 96 How to avoid errors when a key is not found in a dictionary (command: get)
Lecture 97 How to have the same value while the keys differ
Lecture 98 How to unite two dictionaries (double asterisk)
Lecture 99 How to create a dataframe using a dictionary
Section 18: Numpy Arrays
Lecture 100 How to concatenate arrays
Section 19: Series
Lecture 101 How to define a series object that has a constant value
Section 20: Dates
Lecture 102 How to update a value in a DateTime index in a dataframe.
Lecture 103 Using the Workalendar package for country-specific Dates
Lecture 104 Use timedelta() for time conversaions
Section 21: Functions
Lecture 105 Count how many times a function is called
Lecture 106 Another way: Number of times a function is called through lists
Section 22: Bonus
Lecture 107 Extras
Entrepreneurs,Economists,Quants,Members of the highly googled giannelos dot com program,Investment Bankers,Academics, PhD Students, MSc Students, Undergrads,Postgraduate and PhD students.,Data Scientists,Energy professionals (investment planning, power system analysis),Software Engineers,Finance professionals
Screenshots

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