Data science training Los Angeles CA

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 !

Please find my Data Science Course details below:

Data Science Course details:

Mode: Online(Either through Zoom or GoTo Meeting)
Contact: tutordatascience@gmail.com (or) +91 8367299271

Timings:6:30AM to 8:30AM IST(Only weekdays)

1.Python Programming
2.R Programming

• 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

Website : https://honingds.com