Advanced Data Analysis Techniques

data_analytics
  • Course Duration: 4 Months
  • Next Deadline: 15 Feb 2023
  • Starting Date: 04 Mar 2023
  • Fee: PKR 45,000.00*
  • Level: Intermediate Level
  • Pre-requisite: Data Analytics L1
  • Enrolled Candidates: 6247
START LEARNING
Join Now
Financial support available*

About

Elevate your data analytics skills to new heights with Data Analytics – Level 2: Advanced Data Analysis Techniques. Building on the foundational knowledge acquired in Level 1, this intermediate course is designed to deepen your understanding of advanced analytics techniques and tools. Participants will delve into the realm of machine learning, mastering the art of feature engineering, and honing their skills in model evaluation.

The course places special emphasis on the practical application of analytics in solving complex problems. Participants will not only gain theoretical knowledge but will also apply their skills through hands-on projects that simulate real-world scenarios. By the end of this course, students will be equipped to tackle a diverse range of analytical challenges with confidence.

Case Studies - Data Labs - Projects

Welcome to SoftoSmith! Explore the world of data analytics through our course featuring 15 diverse case studies. These real-world examples offer insights into our problem-solving approach and demonstrate our success in various scenarios. Discover our commitment to excellence and innovation in data analytics. If you have specific questions or want to delve into a case, reach out—we’re here to guide you!

What you will learn

  • Introduction to data analysis
  • Inquire to enable data informed decision
  • Get data ready for exploration
  • Transform data from unclean to flawless
  • Examine data to address inquiries
  • Convey data through the skill of visualization
  • Analyzing data using the R programming language
  • Finish a case study as part of the Data Analytics

Details to know

  • Shareable certificate
  • Assessments
  • 3 Final Projects
  • Internships

Modules

Perform a self-evaluation of analytical thinking by providing particular instances of how analytical thinking has been applied.

Utilize Excel for fulfilling fundamental responsibilities of a data analyst, such as inputting and arranging data.
Explain databases, highlighting their functions and components, while incorporating effective methods for organizing data.

Implement fundamental SQL functions to cleanse string variables within a database.

Explain the significance of arranging data before analysis, incorporating the use of sorts and filters.
Explain the application of data visualizations in communicating data and the outcomes of data analysis.
Explain the R programming language and its programming environment.
Distinguish between a capstone, case study, and a portfolio.