Go through this page to know about data science course syllabus, using Python and R. If you want to rapidly master data science, you should focus on the “best” tools.
When you’re learning data science, there are lots of tools and techniques that are a waste of time. Much like with renaming variables, there’s often many ways to accomplish a given task.
If you want to rapidly master data science content, you should focus on the core concepts within your topic syllabus.This will save you massive amounts of time.
Is this syllabus upto-date ?
I have been a trainer for more than 5 years teaching various courses like Python,R, Scala, Statistics,Machine Learning,Hadoop and Apache Spark.
During this period, I have updated the data science syllabus multiple times to ensure it is industry ready.
Since I have trained a lot of professionals ranging from Students,Developers,Architects,Analysts and Project Leads from multi national companies, I know what should be covered as part of the core content.
I am happy to say that during all these years, all my students are 100% satisfied and working as Data Scientists without depending on others during interviews and jobs.
I don’t teach in any institutes & all my trainings are completely online !
Please find my course content details below:
Mode: Online(Either through Zoom or GoTo Meeting)
Contact: email@example.com (or) +91 8367299271
Timings:7AM to 9AM IST(Only weekdays)
Data Science Course Content:
- Python Basics – arithmetic operators, variables, data types, the type function, using a list to store multiple values, creating lists with values, comments, accessing elements in a list, retrieving the length of a list, slicing lists.
- Files and Loops – Reading in files, splitting, loops, list of lists, splitting elements in a list, accessing elements in a list of lists the manual way, looping through a list of lists.
- Booleans and If statements – boolean operators, booleans with greater than, booleans with less than, if statements, nesting if statements, if statements and for loops.
- List Operations – parsing CSV file, getting a single column from the data, counting the items in a list, removing the header, the in statement.
- Dictionaries – practice populating a dictionary, practice indexing a dictionary, defining a dictionary with values, modifying dictionary values, the in statement and dictionaries, the else-statement, practicing with the else-statement, counting with dictionaries.
- Introduction to Functions – why functions, writing our own functions, functions with multiple return paths, functions with multiple arguments, optional arguments, calling a function inside another function.
- Guided project 1 using Jupyter Notebook
- Modules – importing using an alias, importing a specific object, variables within modules, the csv module.
- Classes – Defining a class, passing additional arguments to the initializer, adding additional behavior, enhancing the initializer, make-objects-human-readable
- Error Handling – Defining sets, handling missing values, try-except blocks, exception instances, the pass keyword.
- List Comprehensions – enumerate, list comprehensions, None object, comparing with one, the items method.
- Coding challenge 1
- Variable Scopes – built-in functions, overwriting a built in function, scopes, scope isolation, scope inheritance, inheritance limits, built in inheritance, global variables, inheritance rules.
- Regular Expressions – wildcards in regular expressions, searching the beginnings and endings of strings, reading and printing the data set, counting simple matches in the dataset with re, using square brackets to match multiple characters, escaping special characters, combining escaped characters and multiple matches, adding more complexity to your regular expression, combining multiple regular expressions, using regular expressions to substitute strings, matching years with regular expressions, repeating characters in regular expressions.
- Coding challenge 2
- Dates in Python – converting timestamps, utc, timedelta, formatting dates, parsing dates, reformatting data.
- Guided project 2
- Object oriented programming – defining custom classes, more interesting instance properties, instance methods, class methods, understanding inheritance, overloading inherited behavior.
- Exception handling – organizing our code, overview of exceptions, handling exceptions, overloading comparison operators.
- Lambda functions – omitting starting or ending indices, skipping indices in a slice with steps, negative indexing, searching for substrings, first class functions, more uses for first class functions, lambda functions.
- Guided project 3
- introduction to programming in r – evaluating expressions in r, adding notes to your code using comments, assigning values to a variable, performing calculations using variables, creating vectors, using a function to calculate the mean, performing operations on vectors.
- working with vectors – numeric and character data types, naming elements of a vector, indexing vectors using names, comparing values and logical data types, comparing single values against vectors, logical indexing, performing arithmetic with vectors, vector recycling, appending elements to a vector.
- working with matrices – combining vectors into matrices, naming matrix rows and columns, finding matrix dimensions, adding columns to matrices, indexing matrices by element, subsetting matrices by rows and columns.
- working with lists – anatomy of a list, assigning names to list objects, indexing lists, modifying list elements, adding elements to lists, combining lists.
- working with data frames – installing packages, importing data into r, tibbles specialized data frames, indexing data frames, selecting data columns, adding a new column, filtering by a single condition, filtering by multiple conditions meeting at least one criterion, filtering by multiple conditions, arranging data frames by variables.
- guided project- install r studio, installing r, working in the console, the global environment, importing data, writing scripts.
- working with control structures – importing the data, selection – writing conditional statements, repetition- writing for loops, looping over rows of a data frame, nested control structures, storing for loop output in objects, more than two cases, more than two cases writing a for loop.
- working with vectorized functions – how does vectorization make code faster, a vectorized function for if else statements, multiple cases nesting functions to chain if else statements, functions for solving split-apply-combine problems, grouping and summarizing data frames, summarizing a data frame by multiple variables, chaining functions together using the pipe operator.
- writing custom functions – anatomy of a function, when to write a function, writing functions with two variables as arguments, writing functions for conditional execution, functions with more than two arguments.
- working with functionals – functionals from the tidyverse purrr package, using functionals to apply custom functions, functionals to return vectors of specified types, functionals for two variable functions, functionals for returning vectors of specific types from functions with two variables, functionals for functions with more than two variable arguments.
- fundamentals of string manipulation – subsetting strings by position, splitting strings, combining strings, padding strings.
- Guided project using R
- Understanding Numpy ndarrays
- Selecting and slicing rows and items from ndarrays
- Selecting columns and custom slicing ndarrays
- Vector math
- Arithmetic numpy functions
- Calculating statistics for 1-d ndarrays
- Calculating statistics for 2-d ndarrays
- Adding rows and columns to ndarrays
- Sorting ndarrays
- Numpy Boolean arrays
- Boolean indexing with 1-d ndarrays
- Boolean indexing with 2-d ndarrays
- Assigning values in ndarrays
- Assignment using boolean arrays
- Two guided projects with Numpy
- Introducing dataframes
- Selecting columns from a dataframe by label, using loc method
- Column selection shortcuts
- Pandas Series
- Selecting items from a series by label
- Selecting rows from a dataframe by label
- Series and dataframe describe methods
- Other data exploration methods
- Assignment with Pandas
- using boolean arrays to assign values
- Guided project 1 with Pandas
- Exploring data with Pandas
- Using iloc to select by integer position
- Reading csv files with Pandas
- Working with integer labels
- Using Pandas methods to create boolean masks
- Using boolean operators
- Pandas index alignment
- Using loops in Pandas
- Guided project 2 with Pandas
Data Cleaning with Pandas (Advanced)
- Cleaning column names
- Converting string columns to numeric
- Practise converting string columns to numeric
- Extracting values from the start of strings
- Extracting values from the end of strings
- Correcting bad values
- Dropping missing values
- Filling missing values
- Coding challenge
- Reordering columns and exporting clean data
- Guided project on Data Cleaning
Data Visualization Syllabus
- generating line charts,
- introduction to matplotlib,
- rotating axis ticks,
- adding axis labels ,
- add a plot label with title,
- creating matplotlib figures,
- adding subplots,
- grid positioning,
- generate line chart with Axes object,
- Changing the dimensions of the figure with the figsize parameter ,
- Specifying the color for a certain line ,
- formatting and spacing,
- overlaying line charts,
- adding a legend,
- generating bar plot,
- creating bars,
- aligning axis ticks and labels,
- horizontal bar plot,
- scatter plot,
- switching axes ,
- histograms and box plots,
- frequency distribution,
- binning, histogram in matplotlib,
- comparing histograms,
- box plot,
- multiple box plots,
- guided project 1 on data visualization,
- improving plot aesthetics,
- data ink ratio,
- hiding tick marks,
- hiding spines,
- color layout and annotations,
- setting line color using rgb,
- setting line width,
- improve the layout and ordering,
- replacing the legend with annotations,
- annotating in matplotlib,
- guided project 2 on visualization,
- introduction to seaborn,
- creating histograms in seaborn,
- generating a kernel density plot,
- modifying the appearance of the plots,
- conditional plots ,
- adding a legend,
- installing basemap,
- workflow with basemap,
- generating a scatter plot,
- customizing the plot using basemap,
- introduction to great circles.