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Market Research and Data Analysis using Python

Market Research and Data Analysis using Python

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December 07, 2021

Course Description

Marketing Research will enhance to grab the potential opportunities by understanding the market culture. As one of the growing occupational domains it is the backbone of business strategy and a secret weapon that builds insights of the real market scenario.

The course of Marketing Research will enhance to grab the potential opportunities by understanding the market culture. As one of the growing occupational domains it is the backbone of business strategy and a secret weapon that builds insights of the real market scenario. The increasingly competitive market requires sophisticated marketing intelligence. This can be attained by systematic learning of the fundamentals and having a clear idea about the concept of Marketing Research.

The step-by-step content of the course with real case studies and practice sessions will enhance your understanding and make you well acquainted regarding market research. At the end of the course, you will be awarded a certificate of successful course completion by qualifying our examination. Two to Three Months internship will be provided to the candidates at the end of the course which will provide them a real industry exposure.

Classes

Tuesday and Friday

From 6:30 pm

to 8:30 pm

Fees

INR. 7999/- Only

( Payable Once )

Duration

30 Hours

INTERNSHIP AT INDUSTRY: 2-3 MONTHS

Our Course Features
1
Entire course is 100% LIVE, Instructor Led program.
2
Real Industry driven Case Study will be explained
3
Life time access of the study Materials.
4
Powerful Libraries like,
numpy, matplotlib, seaborn, pandas will be on your fingertips
5
Hands-on experience are more prioritize along with Theory elaboration
6
Internship will be offered at the End of the course for 2-3 Months in Reputed MNCs
7
This Course is designed for all students who are inexperience in coding and belonging to any other technical as well as non-technical disciplines
8
Industry related Project, Data Analysis
along with Report preparation will be
demonstrated
Market Research and Data Analysis using Python Course Content

Session I (13 Hours)

Module 1: Introduction and Approach to Marketing Research
  • What is Marketing Research, Why we use marketing research? Purpose of market research, Features of market research, Different types of research, Marketing research process, Marketing research and social media. Activities related to the study.
  • Process of defining marketing research problem and its importance, Tasks involved in the process of problem definition, Environmental factors that influence in decision making and defining market research problem, Difference between management problems and marketing research problems with examples, Components of developing research approach with real examples, Activities and cases related to the study.
Module 2: Research Design: Exploratory, Descriptive and Causal research design
  • Definition and classification of research design, Exploratory, Descriptive and Causal research and relation between them, Merits and demerits of longitudinal and cross-sectional research designs, potential sources of error. Activities related to the study.
  • Types of data, Sources of data, Application of primary and secondary data in various research, Primary versus secondary data, Criteria for evaluating secondary data, Activities related to the study, Marketing Research project.
  • Qualitative and Quantitative research- features and purpose, Focus Group Interviews, Depth Interviews, Projective Techniques, Ethnography, Case studies and activities, Research projects.
  • Descriptive research designs- Survey and Observation, types of survey methods and observation methods, Comparison of survey and observation, Evaluation of survey, Mystery shopping.
  • Causal research design- concept, conditions. Definition of symbols, Validity in experimentation, Extraneous variables, Controlling extraneous variables, Experimental design, Comparison on experimental and non-experimental design, Test marketing. Case study and activities related to the study.
Module 3: Measurement and Scaling Techniques
  • Scale characteristics and levels of measurements, Primary scales of measurements, Comparison of scaling techniques, Comparative scaling techniques- Rank order, Paired comparison, Constant sum, Q-Sort.
  • Noncomparative scaling techniques, Continuous rating scale, Itemized rating scale, Multi – item scales, Scale Evaluation, Choosing a scaling technique
Module 4: Questionnaire Design
  • Questionnaire and Observation forms, Questionnaire design process, Unstructured and structured questions, Choosing the question wording, Determining the order of questions, Form and Layout, Reproduction and Pretesting. Activity and problems related to the study.
Module 5: Sampling Techniques
  • Sampling design process, Target population, Determining Sampling frame, select sampling technique, determine sample size, Execute sampling process.
  • Non probability and Probability sampling techniques- comparison and use, Internet sampling, Sampling distribution, Statistical approach to determine sample size, Confidence Interval Approach, Adjusting statistically determined sample size, Calculation of response rates, Nonresponse issues in sampling, Adjusting nonresponse issues.
Module 6: Data collection, Field work and Data preparation.
  • Field work or data collection process- supervision, validation, evaluation. Data preparation process- editing, coding, transcribing data. Data cleaning, statistically adjusting data, Selecting data analysis strategy.

Session II (17 Hours)

Module 1: Introduction to Python
Fundamental of Python
  • Why Python and Use of Python in different domains such as Data science, Cyber security, Machine Learning, Food Technology, Finance & Controlling, Management etc.
  • Introduction of ‘Python code writing’ Environment and different IDEs.
Python Step
  • Installation of Anaconda in Windows and LINUX.
  • Activating Jupyter Notebook and spider.
  • First Programming on Python (Hello World).
Module 2: Introduction of pandas Library and Dataset
  • Basic Insights from Datasets and Understanding of Dataset.
  • Python package for data science and let us know Dataframe in pandas.
  • Import our first CSV file using import and read_csv () method and Inspection of datafile.
  • Key features of Jupyter Notebook for pandas library.
  • Data selection from CSV File-
    • All Rows all Columns
    • All Rows selected Columns.
  • Use of loc() and iloc() functions in pandas.
  • iloc()- for Row selection (Used for Rows Selection).
    • Selected Rows and selected Columns.
    • Selected Rows and Selected column using loc().
  • How to Index and Re-index a loaded CSV file.
Module 3: Condition Based Data Filtering in pandas Library
  • Hands-on Filtering Dataframe using one condition.
  • Hands-on Different Operator and Filtering Dataframe using Multiple conditions.
  • Removing Elements from Dataframe
    • Removing Columns
    • Removing Complete Rows
  • How to Add New Columns in Dataset (Adding Scaler Values, Create a new column).
Module 4: Dataset Pre-processing and Data cleansing using pandas
Section-A (Data Cleansing):
Anomalies need to be taken care to handle Data Cleansing, but not limited to-
  • Removing inconsistent data from the dataset.
  • Dealing Missing Values and Removing Missing Values from the dataset.
  • Dealing Duplicate Values and Removing Duplicated Values.
  • Deal with Outliner and how to handle dataset outliners.
Section-B (Feature Extraction)
Module 5: Data Visualization using python, an introduction to matplotlib and seaborn Library
  • Introducing matplotlib and seaborn libraries in python.
  • Using Matplotlib Library Draw Line Chart, Bar Chart, Pie Chart, Histogram, Scatter Plot, Box Plot on different Dataset.
  • Using Seaborn Library implement distplot(), Seaborn Jointplot (), Seaborn Kernel Distribution, Seaborn Heatmap.
Module 6: Statistical Analysis for Decision Making
  • Understanding Numpy Python package
  • Introduction of Population, Sample, Events and Observation using Numpy Library.
  • Different types of Frequency Distribution used in Analytics such as Histogram, Relative Frequency Distribution, Cumulative Frequency Distribution.
  • Insights of Central Tendency using Numpy which includes, Mean, Median, Mode, Geometric Mean.
  • Understanding Dispersion like, Range, Variance, Standard Deviation of both Population & sample Dataset.
Module 7: Advanced Statistical Analysis for Decision Making
  • Illustration of Some Advanced Frequency Distribution namely, Uniform Frequency Distribution (Discrete and Continuous Frequency Distribution).
  • What is Normal Distribution and Hands-on Probability Density Function and Cumulative Distribution Function.
  • Standard Normal Distribution-Probability and Z- value.
  • Hands-on Covariance and Correlation with an Example of relevant dataset (Post implementation of Data Cleansing).
Module 8: Project on Business Analysis

We will perform the Data Analysis on the Finance related Dataset or any other suitable Dataset using pandas, Numpy library and we will see the visual representation of the Analysis by means of various chart depiction as well.

data analysis using python

data analysis using python

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