07/12/2024 updated
ML
100 % available
Data Analyst
Pune, India
Worldwide
Bachelor of EngineeringAgile MethodologyData AnalysisBusiness LogicMicrosoft AzureBig DataEncodingsDatabasesCustomer Data ManagementInformation EngineeringExtract Transform Load (ETL)Data SystemData VisualizationPython (Programming Language)Logistic RegressionMachine Learning
agile methodologies, Agile, Azure Data Factory, Azure data lake, Azure Machine Learning, Azure Synapse Analytics, large datasets, business logic, customer data, Exploratory Data Analysis, data analysis, Data analytics, Data lakes, data manipulation, data solutions, data visualization, databases, database, electronic health records, encoding, ETL, feature engineering, feature selection, data engineering, logistic regression, machine learning, Matplotlib, Azure, Microsoft Dynamics, Microsoft Dynamics 365, MS Excel, Microsoft Office 365, MS PowerPoint, MS Word, MySQL, NumPy, Pandas, Power BI, PowerBI, Python data science libraries, Python, subqueries, SQL, SQL database, Scikit Learn
Experience
Professional experience0 - 5 years
Freelancer since2021
Languages
EnglishFluent
Project history
PROJECTS AND ASSIGNMENT
Project 1: Housing Price Prediction System
Description : The project involves the analysis of the California housing dataset to
determine the approximate prices for houses. The primary goal is to
predict the median house prices for different locations in California. This
type of analysis is valuable for both homebuyers and sellers, providing
insights into the factors influencing housing prices and facilitating
informed decision-making in the real estate market.
Role : Data Analyst
Responsibilities :
* Python Tools/Libraries Used
* NumPy: Used for numerical operations, including mean calculations.
* Pandas: Used for data manipulation and handling.
* Matplotlib and Seaborn: Employed for data visualization
* To assess the trend and predict housing prices based on covariates such as housing age, total
bedrooms, ocean proximity etc.
* Data understanding and cleaning.
* Creating appropriate plots for visualization and to check the distribution of data.
* Handling missing data through deletion or imputation.
* Creating new datasets based on specific criteria.
* Create a user-defined function for calculating the median.
* Applying logical conditions to create new columns.
* Interpretation and communication of findings.
Project 2: Axon Classic Cars Sales Analysis System
Description : A small company Axon, which is a retailer selling classic cars, was facing
issues in managing and analysing their sales data. The sales team was
struggling to make sense of the data and they do not have a centralized
system to manage and analyze the data. The management was unable to
get accurate and up-to-date sales reports, which was affecting the
decision-making process. To address this issue, the company has decided
to implement a Business Intelligence (BI) tool. The goal of the capstone
project is to design and implement a BI solution using PowerBI and SQL
that can help the company manage and analyze their sales data
effectively.
Role : Data Analyst
Responsibilities :
* Started by getting the sales, product, order, customer, employee and payment details from their
database using SQL.
* Next, the SQL database was connected to Power BI via a connector, and data was transformed
using Power Query.
* Clean and transform the data to make it ready for analysis. This involves tasks such as
removing duplicates, handling missing values, and ensuring data consistency.
* Data modeling to establish relationships among essential data points for a cohesive and
insightful analysis.
* Power BI's quick measure feature and DAX functions to analyze the data.
* Creating a dashboard to visualize sales data using PowerBI.
* It includes charts, graphs, and tables that provide insights into sales performance over time,
customer demographics, and product popularity.
* Get a user-friendly dashboard that offers valuable insights into sales, customer profiles, order
details, product details, payments, and employee performance based on sales.
Project 3: Credit Card Approval System
Description : Machine learning model developed for banks, that gives
creditworthiness of applicants by analyzing historical data and
identifying patterns associated with good and bad clients. This will
minimize risks, allowing banks to make informed decisions on credit
approvals. A top focus for the bank has always been acquiring new credit
card customers. Giving out credit cards without doing proper research or
evaluating applicants' creditworthiness is quite risky.
Role : Data Analyst
Responsibilities :
* Data Importing and Data Preprocessing.
* Handle missing values by imputing or removing them.
* Address outliers in the data to prevent skewing of results.
* Exploratory Data Analysis (EDA), Generate descriptive statistics, visualizations, and
correlations. Explore relationships between variables to inform feature selection.
* Missing Values Imputations techniques such as mean, median, or machine learning-based
methods to fill missing values.
* Outlier Treatment through methods like trimming, winsorizing, or transforming data.
* Feature Transformation: Apply techniques like one-hot encoding, label encoding, or feature
engineering to prepare the features for modeling.
* Implement techniques such as oversampling, undersampling, or using synthetic data to deal
with imbalance the dataset.
* Feature Scaling methods such as Min-Max scaling or Standardization to ensure that features
contribute equally to the model.
* Select appropriate algorithms (e.g., logistic regression, decision trees, or ensemble methods).
* Split the data into training and testing sets.
* Evaluate different models using metrics like accuracy, precision, recall, and F1-score.
* Select the model with the highest performance.
* Testing & Optimizing: Test the model on a separate test dataset.
* Fine-tune hyperparameters or adjust the model based on testing results.
Project 4: HealthCare 360 Solution, UK
Description :This project was developed for NHS healthcare customer. Microsoft
Dynamics Customer Insights Health care 360 solution project
developed for them. A Microsoft Dynamics Customer Insights 360
solution project for a healthcare database is a project to implement a
customer data platform (CDP) that will unify patient data from all
disparate sources within the healthcare organization, including
electronic health records (EHRs), patient portals, and claims data. Once
the CDP is in place, the healthcare organization will be able to create a
single, holistic view of each patient, which can then be used to improve
patient care, reduce costs, and increase operational efficiency.
Role : Data Analyst
Responsibilities :
* Gathering and analyzing client requirements to ensure clarity of scope, capture business logic
etc.
* Designed and implemented a Microsoft Dynamics 365 Customer Insights solution to unify
customer data from CRM and other automation systems.
* Data assessment and planning: This involves identifying all of the disparate sources of patient
data within the healthcare organization and developing a plan for unifying and cleaning the
data.
* CDP implementation: This involves implementing Microsoft Dynamics Customer Insights and
configuring it to meet the specific needs of the healthcare organization.
* Patient segmentation: This involves building and refining patient segments based on
demographic, clinical, and behavioral data.
* Reporting and analytics: This involves developing and delivering reports and analytics that
provide insights into patient behavior and trends.
* Sprints, scrum calls and prepare for Agile frameworks and help scrum masters and product
owners.
* Creating Power BI reports and charts.
* Work closely with development and business team to understand requirement to ensure
endtoend quality assurance of software product builds.
Other Project:
Analysis of Instagram Clone Dataset using MYSQL :
The aim of this project was to identify the oldest users, most liked photos on Instagram, day of the week
where most users registered, and most common hashtags used. Various queries were executed in MySQL,
and a number of insights were obtained.
Project 1: Housing Price Prediction System
Description : The project involves the analysis of the California housing dataset to
determine the approximate prices for houses. The primary goal is to
predict the median house prices for different locations in California. This
type of analysis is valuable for both homebuyers and sellers, providing
insights into the factors influencing housing prices and facilitating
informed decision-making in the real estate market.
Role : Data Analyst
Responsibilities :
* Python Tools/Libraries Used
* NumPy: Used for numerical operations, including mean calculations.
* Pandas: Used for data manipulation and handling.
* Matplotlib and Seaborn: Employed for data visualization
* To assess the trend and predict housing prices based on covariates such as housing age, total
bedrooms, ocean proximity etc.
* Data understanding and cleaning.
* Creating appropriate plots for visualization and to check the distribution of data.
* Handling missing data through deletion or imputation.
* Creating new datasets based on specific criteria.
* Create a user-defined function for calculating the median.
* Applying logical conditions to create new columns.
* Interpretation and communication of findings.
Project 2: Axon Classic Cars Sales Analysis System
Description : A small company Axon, which is a retailer selling classic cars, was facing
issues in managing and analysing their sales data. The sales team was
struggling to make sense of the data and they do not have a centralized
system to manage and analyze the data. The management was unable to
get accurate and up-to-date sales reports, which was affecting the
decision-making process. To address this issue, the company has decided
to implement a Business Intelligence (BI) tool. The goal of the capstone
project is to design and implement a BI solution using PowerBI and SQL
that can help the company manage and analyze their sales data
effectively.
Role : Data Analyst
Responsibilities :
* Started by getting the sales, product, order, customer, employee and payment details from their
database using SQL.
* Next, the SQL database was connected to Power BI via a connector, and data was transformed
using Power Query.
* Clean and transform the data to make it ready for analysis. This involves tasks such as
removing duplicates, handling missing values, and ensuring data consistency.
* Data modeling to establish relationships among essential data points for a cohesive and
insightful analysis.
* Power BI's quick measure feature and DAX functions to analyze the data.
* Creating a dashboard to visualize sales data using PowerBI.
* It includes charts, graphs, and tables that provide insights into sales performance over time,
customer demographics, and product popularity.
* Get a user-friendly dashboard that offers valuable insights into sales, customer profiles, order
details, product details, payments, and employee performance based on sales.
Project 3: Credit Card Approval System
Description : Machine learning model developed for banks, that gives
creditworthiness of applicants by analyzing historical data and
identifying patterns associated with good and bad clients. This will
minimize risks, allowing banks to make informed decisions on credit
approvals. A top focus for the bank has always been acquiring new credit
card customers. Giving out credit cards without doing proper research or
evaluating applicants' creditworthiness is quite risky.
Role : Data Analyst
Responsibilities :
* Data Importing and Data Preprocessing.
* Handle missing values by imputing or removing them.
* Address outliers in the data to prevent skewing of results.
* Exploratory Data Analysis (EDA), Generate descriptive statistics, visualizations, and
correlations. Explore relationships between variables to inform feature selection.
* Missing Values Imputations techniques such as mean, median, or machine learning-based
methods to fill missing values.
* Outlier Treatment through methods like trimming, winsorizing, or transforming data.
* Feature Transformation: Apply techniques like one-hot encoding, label encoding, or feature
engineering to prepare the features for modeling.
* Implement techniques such as oversampling, undersampling, or using synthetic data to deal
with imbalance the dataset.
* Feature Scaling methods such as Min-Max scaling or Standardization to ensure that features
contribute equally to the model.
* Select appropriate algorithms (e.g., logistic regression, decision trees, or ensemble methods).
* Split the data into training and testing sets.
* Evaluate different models using metrics like accuracy, precision, recall, and F1-score.
* Select the model with the highest performance.
* Testing & Optimizing: Test the model on a separate test dataset.
* Fine-tune hyperparameters or adjust the model based on testing results.
Project 4: HealthCare 360 Solution, UK
Description :This project was developed for NHS healthcare customer. Microsoft
Dynamics Customer Insights Health care 360 solution project
developed for them. A Microsoft Dynamics Customer Insights 360
solution project for a healthcare database is a project to implement a
customer data platform (CDP) that will unify patient data from all
disparate sources within the healthcare organization, including
electronic health records (EHRs), patient portals, and claims data. Once
the CDP is in place, the healthcare organization will be able to create a
single, holistic view of each patient, which can then be used to improve
patient care, reduce costs, and increase operational efficiency.
Role : Data Analyst
Responsibilities :
* Gathering and analyzing client requirements to ensure clarity of scope, capture business logic
etc.
* Designed and implemented a Microsoft Dynamics 365 Customer Insights solution to unify
customer data from CRM and other automation systems.
* Data assessment and planning: This involves identifying all of the disparate sources of patient
data within the healthcare organization and developing a plan for unifying and cleaning the
data.
* CDP implementation: This involves implementing Microsoft Dynamics Customer Insights and
configuring it to meet the specific needs of the healthcare organization.
* Patient segmentation: This involves building and refining patient segments based on
demographic, clinical, and behavioral data.
* Reporting and analytics: This involves developing and delivering reports and analytics that
provide insights into patient behavior and trends.
* Sprints, scrum calls and prepare for Agile frameworks and help scrum masters and product
owners.
* Creating Power BI reports and charts.
* Work closely with development and business team to understand requirement to ensure
endtoend quality assurance of software product builds.
Other Project:
Analysis of Instagram Clone Dataset using MYSQL :
The aim of this project was to identify the oldest users, most liked photos on Instagram, day of the week
where most users registered, and most common hashtags used. Various queries were executed in MySQL,
and a number of insights were obtained.