Biswajit Swain

Detail-focused Data Analyst with 2+ years of experience and knowledge in data warehousing, process validation and business needs analysis. Proven to understand customer requirements and translate into actionable project plans. Dedicated and hard-working .

• Data Analytics Power Pack Training from Nikhil Analytics, Bangalore.
• Predictive Analytics using Python
• Advance Excel Techniques & Data Management
• SQL For Analyst
• Tableau For Analyst
• PowerBi for Analyst

• Father's Name: Manoj ku. swain
• Date of Birth: 09/06/1998

 Project 1-

Analysis of Camera data using Python for Analytics, To analyze the data for different year, how many camera models get purchased, which model customer has preferred which one is most expensive and cheap. As I extracted data from path using Pandas in built function read_csv then checked statistical distribution of data using describe() function it gives me mean(),median(),quantile values for different interval, then checked whether data contains NULL values ,using isnull().sum() function and if data column has missing value above 40% of data ,dropped those column if not replaced those values with median() of respective columns using fillna() function, then checked count of model for every year got sale using groupby() function, which year has maximum sale count checked using max() function. Then I checked maximum price and minimum price of camera using or condition. In this way I summarized dataset.

Project 2 -

Analysis of products and customers using pivot table, Analysis of given data based on different columns such as product, customer name, Quarter, sales. Identify which product is in trend quarter wise, which customer is more frequently buy the products, overall TOP 2 customers and TOP 2 products. In this which products are Top 2 in sales calculated using filters from pivot table and which customer is frequently buying from shops and looking for which product using SUM functions then applied filter on product to select TOP 2, which are the top3 customers and top 5 products those customers always prefers to buy quarterly as season changes food demand also changes as in winter people prefer hot food and in summer cold drinks , based on that in which quarter which product is in more demand and which one is need to restock and which one is remain unsold ,which one is need to replace with other product or on which product need to give discount. 

Project 3 -

AIRBNB Data Visualization using Tableau, First, I have created Count Plot for a single categorical variable “room_type”. Secondly, I have created a count plot for two categorical variables “room_type” and “neighbourhood group”. Thirdly, I have created a count plot for three categorical variables “room_type” ,“neighbourhood group” and “price_group. Fourthly, I have created a scatter plot between “price” and “reviews_per_month”. Finally, I have created a pie chart to find which “room_type” has higher percentage in different “neighbourhood group”.After that, I have created dashboard Objective: AirBnb Dataset includes all needed information to find out more about hosts, geographical availability, and neo learn about different hosts and cities and also to learn about predictions such as locations, price and reviews, which hosts are necessary metrics to make predictions and draw conclusions. the busiest and why? Is there any noticeable difference of traffic among different areas and what could be the reason for it? 

Project 4 -

Creation of Student info tables with constrains using SQL, Create a table in which only valid data will be entered using constrains In Student Info table, I have created the table using create table Student_ Info command, keeping Student Id as persisted Primary key with different columns as in Email column using CHECK constraint checked whether email is valid or not then phone no should have length 10 and it should contains numbers using LIKE verified and both conditions are satisfied or not checked using CHECK then Course_opted1,Course_opted2,Course_opted_3 columns made unique applying UNIQUE constraint together on these column and make sure that opted course should be within given list. R marks info table, SQL_marks_info table, Excel marks info, VBA_marks_info, Tableau Marks info, Python Marks info, ML marks info , Full Length marks _info table and Placement Activity Table are created using create table tablename command using Student Id as Foreign key referencing Student Id of Student Info table.Perform joins ,some aggregate functions,sort and filter records to get correct output.