Data Analytics for Calls data

Data Analytics for Calls data

Most data analyst, data science, and coding courses miss a critical practical step. They don’t teach you how to work with raw data, how to clean, and preprocess it. This creates a sizable gap between the skills you need on the job and the abilities you have acquired in training. Truth be told, real-world data is messy, so you need to know how to overcome this obstacle to become an independent data professional.

Most bootcamps and live training sessions neglect this aspect and show you how to work with ‘clean’ data. But this isn’t doing you a favor. In reality, it will set you back both when you are applying for jobs, and when you’re on the job.

The goal of this course is to provide you with complete preparation. And this course will turn you into a job-ready data analyst - provided that you are already familiar with basic analytics.

Learning Outcome

At the end of this programme, participants should be able to:

1. Identify and prepare data from the correct sources in Contact Management Centers such as Calls Data and social media queries data (email & live chat)
2. Analyze data using appropriate methods and tools.
3. Interpret data as an input for the decision making process.
4. Collect raw data and transform into data set
5. Understand the data and process it to dataset or logical figure
6. Explore the pattern of data in decision making and how to interpret data into graph, charts and others for presentation
7. Convey the benefits that interaction analytics can bring to our Contact Center to others
8. Transform agent performance through contact, screen and speech analytics
9. Analyze customer communications to discover service hiccups early
10. Deal with the challenges of designing and effective analytics programme
11. Explore best practice at other Call Centers

Pre-Requisites

• Familiar with data analytics.
• Familiar with analytics tools such as Pandas.
• Basic practical analytics skills.

Course Outline

The topics we will cover

1. Theory about the field of data analytics
   1. Introduction to the World of Business and Data
   2. Relevant Terms Explained
   3. Data Analyst Compared to Other Data Jobs
2. Pandas
   1. Label-based vs Position-based Indexing
   2. Working with Indices in Python
   3. Using Methods in Python
   4. Parameters vs Arguments
   5. Introduction to pandas Dat Sets
   6. Introduction to pandas DataFrames
   7. Creating DataFrames from Scratch
3. Working with text files and flat data
   1. File vs File Object, Read vs Parse
   2. Structured vs Semi-Structured and Unstructured Data
   3. Data Connectivity through Text Files
   4. Fixed-width Files
   5. CSV Files with pandas
   6. Importing Data with the "index_col" Parameter
   7. Importing *.json Files
4. Data collection
   1. Tactics
   2. Implementation
5. Data cleaning
6. Data preprocessing
7. Data Presentation