VIRTUAL ATTENDANCE- Training on Data Science with Python and R Course

INTRODUCTION.

The Data Science community today have people who generally work with only a single language. However, there are still those who are using both Python and R, but their percentage is small. On the other hand, there are a lot of people who are committed to only one programming language but wished they had access to some of the capabilities of their adversary. For instance, R users sometimes yearn for the object-oriented capacities that are native to Python and similarly, some Python users long for the full range of the statistical distributions that are available within R.

Data science, in simple words, is the field of study that involves collecting, analyzing, and interpreting large sets of data to uncover insights, patterns, and trends that can be used to make informed decisions and solve real-world problems.

R is a programming language for statistical computing and data visualization. It has been adopted in the fields of data mining, bioinformatics, and data analysis. The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data. R is widely used in data science by statisticians and data miners for data analysis and the development of statistical software. R is one of the most comprehensive statistical programming languages available, capable of handling everything from data manipulation and visualization to statistical analysis.

Python is a programming language widely used by Data Scientists. Python has in-built mathematical libraries and functions, making it easier to calculate mathematical problems and to perform data analysis. Data wrangling and manipulation are essential steps in data analysis. Python's NumPy library provided that tools for working with arrays, such as indexing, slicing, and reshaping arrays. NumPy also provides tools for mathematical operations on arrays, such as addition, subtraction, multiplication, and division.

TARGET AUDIENCE

This course is intended for Mathematicians, Staticians, Python developers, data scientists, Computer scientists, Economists, Data analysts, Data collectors, Researchers , Data consultants, IT experts, Bankers, Machine learning, finance and anyone who would like to learn data science in R and Python.

DURATION

6 days

PREREQUISITES

Before attending this course, it is recommended that students:

• Have basic computer navigation skills
• Prior programming experience is not necessary, but it can help to put these topics in perspective

COURSE OBJECTIVE

After completing this course, students will be able to:

• The basics of statistical computing and data analysis
• How to use R for analytical programming
• How to implement data structure in R
• R loop functions and debugging tools
• Object-oriented programming concepts in R
• Data visualization in R
• How to perform error handling
• Writing custom R functions 
• Know basic data types in Python.
• Know operators, how to clean and merge datasets.
• Know pandas library, the main methods for DataFrames.
• Know how to import data in Python.
• Know how to work in Jupyter Notebook.

COURSE CONTENT

Module I

An introduction to Python

• Introduction to Python
• How to use IDLE to develop programs 
• Basic coding skills
• How to work with data types and variables
• How to work with numeric data
• How to work with string data
• How to use five of the Python functions

  How to code control statements

• How to code boolean expressions
• How to code the selection structure
• How to use the iteration structure

  How to define and use functions and modules

• How to define and use functions
• More skills for defining and using functions
• How to create and use modules
• How to use standard modules
• How to plan the functions of a program

Module II

How to test and debug a program

• An introduction to testing and debugging
• Four techniques for testing and debugging
• How to use the IDLE debugger

How to work with lists and tuples

• Basic skills for working with lists
• How to work with a list of lists
• More skills for working with lists
• How to work with tuples

How to work with file I/0

• An introduction to file I/O
• How to use text files
• How to use CSV files
• How to use binary files

How to handle exceptions

• How to handle a single exception
• How to handle multiple exceptions

 How to work with numbers

• Basic skills for working with numbers
• How to format numbers
• How to work with decimal numbers

 How to work with strings

• Basic skills for working with strings
• How to split and join strings

 How to work with dates and times

• How to get started with dates and times
• More skills for working with dates and times

Module III

Introduction to R

• Why use R?
• Obtaining and installing R
• Working with R
• Packages
• Batch processing
• Using output as input—reusing results
• Working with large datasets

Data Entry, management and Manipulation with R

• Creating a dataset
• Understanding datasets
• Data structures
• Data input
• Annotating datasets
• Useful functions for working with data objects
• Creating new variables
• Recoding variables
• Renaming variables
• Missing values
• Date values
• Type conversions
• Sorting data
• Merging datasets
• Subsetting datasets
• Using SQL statements to manipulate data frames

Module IV

Tabulations and Graphics with R

• Graphing Qualitative data
• Graphing Quantitative data
• Getting Started R Graphics
• Working with graphs
• A simple example
• Graphical parameters
• Adding text, customized axes, and legends
• Combining graphs
• Basic Graphs (Bar plots Pie charts, Histograms, Kernel density plots, Box plots, Dot plots)
• Intermediate graphs (Scatter plots, Line charts, Correlograms, Mosaic plots)
• Frequency and contingency tables

Quantitative Analysis using R

• Descriptive statistics
• Correlations
• t-tests
• Nonparametric tests of group differences
• Visualizing group differences
• Regression
• Analysis of Variance
• Power Analysis

Module V

Basic statistical terms and concepts

• Basic data quality checks
• Basic exploratory data analysis procedures
• Basic Descriptive Statistics
• The core functions of inferential statistics
• Common inferential statistics
• Concepts and Software for Data Processing
• Data Processing using Census and Surveys Processing Software (CsPro)
• Use of Mobile Phones for Data Collection and Processing

Advanced Statistical Analysis

• Generalized Linear Models
• Principal components and factor analysis
• Advanced methods for missing data

METHODOLOGY

The instructor led trainings are delivered using a blended learning approach and comprises of presentations, guided sessions of practical exercise, web based tutorials and group work. Our facilitators are seasoned industry experts with years of experience, working as professional and trainers in these fields.

All facilitation and course materials will be offered in English. The participants should be reasonably proficient in English.

ACCREDITATION

Upon successful completion of this training, participants will be issued with an Livecode Technologies  certificate.

TRAINING VENUE

The training is residential and will be held at livecode Training Centre. The course fee covers the course tuition, training materials, two break refreshments, lunch, and study visits.

All participants will additionally cater for their, travel expenses, visa application, insurance, and other personal expenses.

Email: This email address is being protected from spambots. You need JavaScript enabled to view it..

Mob: +254 725771853

TRAINING FEES.

The course fees is KES 45,000.00 or USD 650.00 exclusive of VAT. 

PAYMENT

Payment should be transferred to Livecode Technologies account through bank on or before the training date.

Send proof of payment to This email address is being protected from spambots. You need JavaScript enabled to view it.

CANCELLATION POLICY

Payment for the all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

1. Participants may cancel attendance 14 days or more prior to the training commencement date.
2. No refunds will be made 14 days or less to the training commencement date. However, participants who are unable to attend may opt to attend a similar training at a later date, or send a substitute participant provided the participation criteria have been met

Please Note: The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.