I am an online
Data Science trainer, have huge experience in Data Science, Python, R, Scala and Big Data Stack.
I have been a trainer for more than 5 years teaching various courses like Python,R, Scala,Statistics,Machine Learning,Hadoop and Apache Spark.
I have given
Data Science online training for the last couple of years, trained a lot of professionals ranging from Students,Developers,Architects,Analysts and Project Leads from Companies like, GE, Genpact, Metlife-USA, HCL,Amazon,Bank of America,Microsoft,US-UK-Germany students.
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 dont teach in any institutes & all my Trainings are completely online !
Mode: Online(Either through Zoom or GoTo Meeting)
Contact: tutordatascience@gmail.com (or) +91 8367299271
Timings:6:30AM to 8:30AM IST(Only weekdays)
- R Installation
- R Studio Installation
- Using a R notebook file
- Programming using Vectors
- Character data type
- Attributes
- Comparing single values against vectors
- Indexing using logical data types
- Performing arithmetic on vector
- Vector recycling rule
- Appending data to vector
- Introduction to Matrices
- Creating matrix
- Vector & Matrix data types
- Naming rows and columns
- Finding dimensions of a matrix
- Creating new columns and rows
- Subsetting a matrix by element
- Returning specific rows and columns from matrix
- Sorting a matrix
- Sorting and previewing data
- Dataframes in R
- Examining the internal structure of dataframes
- Representing categorical values using factors
- Selecting data by rows and columns
- Selecting specific values
- Using comparison operators to filter values
- Combining conditions using logical operators
- Sorting a dataframe in R
- Lists in R
- Naming lists
- Adding values to a list
- Indexing a list
- Changing values in a list
- Merging lists
- R control structures
- If & else statements
- For loops
- Adding results of loop to an object
- Using if else within for loop
- Using while loop
- Introduction to functions
- Nested functions
- Adding control structure to a function
- Apply functions in R
- Using lapply with custom functions
- Using sapply over built in functions
- Using sapply over custom functions
- Using vapply to control returned values
- Using tapply on dataframes and matrices
- R Strings & Dates
- Concatenating strings in R
- Updating column in a Dataframe
- Extracting a substring
- Difference between strsplit and paste()
- Replacing value in a string
- Removing whitespaces from string
- Extracting parts of a date
- Creating a new column in dataframe
- Guided project using R
3.Numpy
- 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
4.Pandas
- 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
5.Data Cleaning with Pandas
- 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
6.Visualization
7.Probability and Statistics
8.Machine Learning
9.Calculus
10.Linear Algebra
11.Linear Regression
12.Decision Trees
Contact Us:
Email : honingds01@gmail.com
The aim of this post is to provide a quick reference to people who want to learn 
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