Python For Data Science

Course Curriculum

Your first assignment consist of various basic concepts of python. This language is important for any learner in order to drive various machine learning based solutions in this course.

This assignment keeps beginners in mind and covers topics like python variables, numeric python operators, Logical Operators, various loop statements(If, while and for) , Functions in python, strings and their operations/functions, list and list comprehension along with reference videos on every topic. Learners can learn by solving problems on each concept of python covered here.

This assignment covers more examples on topics like python variables, numeric python operators, Logical Operators, various loop statements(If, while and for) , Functions in python, strings and their operations/functions, list and list comprehension along with reference videos on every topic. It has additional topics like enumerate, dict comprehension, set comprehension, reduce, map, filter, lambda, scope of variables etc. Learners can learn by solving problems on each concept of python covered here.

In this assignment you will be solving logical questions on python, which will benefit you to crack python coding interview rounds. Involves python problems like solving mathematical equations, strings and string manipulation, playing with numbers and their digits and many more similar interesting coding questions.

You will also get an opportunity to create a mini python project where you will be creating a game called “Hangman” which can act as an Intelligent game, this game has rich use of loops (for, while, if else etc) to apply various conditions, python functions, string inbuild functions, use of break and continue statements and lists.

Since data structures are way of organizing and storing data, hence it becomes important topic. This assignment aims to cover various python data structures and their implementations. Topics which are covered are Lists and it’s operations such as slicing, deleting ,appending, updating etc. List comprehensions, Sets and it’s operations like union, intersection, diferences etc, Tuples and its implementation, Dictionaries and its operations like adding and removing key value pairs, iterating item values etc. Along with handson problem added reference videos on every topic covered.

Numpy aims to provide an array object that is up to 50x faster than traditional Python lists, this assignment will help learners to optimize their code using it. Numpy Assignment covers topics like defining various different dimensions of numpy arrays, Various Numpy functions to create arrays like arange(), eye(), full(), diag(), linespace() etc, Defining Numpy array with random values, Reshaping arrays to different dimensions, Numpy array indexing and slicing, Difference between Numpy copy and view function, Bonus operation on numpy like hstack() and vstack(), Numpy array modifications using insert, delete and append functions, Mathematical operations and searching in Numpy arrays. Also shown practical operation on how arrays are faster than lists. To understand all topics thoroughly, we have added reference links on each topic.

Pandas has functions for analyzing, cleaning, exploring, and manipulating data, which makes it important library for data science. This assignment introduces topics on pandas like pandas series and its operations like sort, append, indexing etc, Pandas dataframes and its operations like accessing existing rows, columns, adding new rows or columns. Converting series to dataframes, Concatenation of one or more dataframes, dataframeelement acess using conditions, dataframe Indexes, loc and iloc, reading csv, merging, groupby and apply function. For more conceptual clarity we also added reference videos for all the topics.

Data Cleaning plays an important role in the field of Data Managements as well as Analytics and Machine Learning. This assignment will give you practical experience on how to handle any dirty data. You will learn how to treat inconsistent/irrelevant columns in the data, Handling Missing values by dropping empty records, imputing missing fields using techniques like forward fill, backward fill, mean imputation, constant imputation, interpolation and knn, Pandas data frame shallow and deep copy methods, Working and optimizing code with iterrows and itertuples, renaming columns with meaningful labels, treating duplicate values, Treating constant( low variance) column values. Implementing Regular expressions on textual data to play with different patterns. For more conceptual clarity also added reference links on each topic.

Regular Expressions, or regex or regexp in short, are extremely and amazingly powerful in searching and manipulating text strings, particularly in processing text data. One line of regex can easily replace several dozen lines of programming code. In this assignment you will be solving easy to hard level regex problems like matching digit and non-digit characters, detecting HTML tags in text, IP address validation, detecting email addresses, detecting domain problems, whitespace and non-whitespace problems, and substring problems. Provided reference video and document links for any assistance. It is widely used in projects that involve text validation, NLP and text mining, hence Regex has become a useful tool to know.

Exploratory Data Analysis is a way of visualizing, summarizing and interpreting the information that is hidden in rows and column format. In this assignment you will be applying Data cleaning techniques which you learned in previous assignment, Method to fetch basic statistical information out of data, Detecting Outliers which pollutes the data, outlier removal techniques like IQR, and Z-score and removing them to make a uniform dataset. You will implement Univariate plots like box plot, Bar plots, Count Plots, Histogram and density plots, Bivariate plots like Scatter plots, Line plots, box plots with respect to third variable and joint distribution plots, also Multivariate plots like Pair plot, multivariate scatter plot, parallel coordinates and Heatmaps. Every topic has a YouTube reference link to give you better conceptual clarity.

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