The SGL’s Data Analysis training course consists of the following modules:
Module 1: Data Analysis with R Programming N150,000.00 3 months
Module 2: Data Analysis with Python N150,000.00 3 months
Data analysis refers to the methods needed to clean, analyze, interpret, and present data using various approaches and analytical tools. It entails converting unprocessed data into informative statistics, summaries, and explanations.
Objectives
- Describe data analytics and the main phases involved in the process.
- Distinguish between several data jobs, including those of business analyst, business intelligence analyst, data scientist, data engineer, and data analyst.
- Describe the various file formats, data sources, and data structure kinds.
- Describe the utilization of Big Data platforms, the ETL process, and several types of data repositories.
- Explain the procedures and equipment used to collect, organize, mine, analyze, and visualize data.
- Enumerate the many job paths in data analysis and provide resources for developing your skills in this area. Show that you have a solid grasp of the collection, manipulation, mining, analysis, and visualization of data.
Course Outline
Module 1: Data Analysis with R Programming
Each session will include a lecture followed by practical.
Participants will also get a chance to see how data analysis is being applied in a real-life scenario.
The following gives a brief summary of what the course will cover.
- Introduction to R: This includes setting up R and R-studio along with any necessary packages, including ggplot2, haven, dplyr, reshape2, readr, and readxl.
- An overview to programming (scripts, basic syntax, and how to document, comment, and share code)
- Bringing in and loading data into R: Additionally, the learner will be able to clean data and save data in R.
- Handling and modifying data: The student will learn how to work with vectors, matrices, dates, and times,
- among other items.Printing conditional tables and filtering, sorting data, appending, merging, and reshaping datasets, as well as concatenating and ordering variables, will all be taught to the learners.
- Analysis of descriptive statistics The learner will be guided through the computation of measures of dispersion (variance, standard deviation, interquartile range, range, coefficient of
Module 2: Data Analysis with Python
- An overview of data analysis using Python
- The Pandas prerequisites for analyzing data
- The necessary Pandas modules for data visualization
- The Seaborn prerequisites for visualizing data
- How to obtain the information
- How to make the data clean
- How to get the information ready
- Methods for Data Analysis
- How time-series data is analyzed
- How to use a linear regression model to make predictions
- How to use a multivariate regression model to make predictions
A data analyst uses technologies for reporting and visualization to help businesses get the most out of their data assets. They are also in charge of cleaning, converting, and profiling data. In addition, they have to enable and integrate advanced analytics capabilities into reports for analysis, as well as develop and construct scalable and efficient data models.
Key insights for the job outlook of data analysts
According to projections, the global market for data analytics is expected to grow from $23 billion in 2019 to $133 billion by 2026, suggesting a significant need for skilled data analysts.
The integration of novel modeling, prescriptive analytic approaches, and machine learning has made data analyst tasks more complicated, necessitating the need for experts to possess a flexible skill set.
Data analysts have bright futures ahead of them, since the industry is expected to rank as the top employment in America in 2022. By 2027, the global market for big data analytics is projected to be valued at $105 billion.
The average salary for a data analyst in the United States as of December 2021 was $69K; however, earnings varied greatly depending on the industry, level of expertise, size of the organization, and
