• Employee performance trend analysis in MySQL:
  Purpose: To analyze the performance of 300 underperforming employees during Q1 and Q2, using panel data.
  Workings: Gathering monthly profit data for each field employee, identifying underperformers for the current month using a specific criterion, calculating the quarterly average profit for each underperformer using monthly panel data, categorizing employees as poor or excellent performers for each quarter, comparing quarterly performance for each employee, and selecting only those who improved in the preceding quarter but performed worse in the subsequent quarter. 
  Accomplishment: Successfully identified poor performers and suggested areas for improvement.

• 1+ other use case solutions in MySQL

• A Machine Learning Project on Classifying Happy or Unhappy People

• Purpose: To find the factors that associate with happiness.

• Workings: Removing unnecessary text from all 31 columns, encoding categorical variables using one-hot, dummy, and ordinal encoding, selecting features using the Boruta feature selection method, selecting training data, building models, creating classification reports and confusion matrices, plotting ROC curves, selecting the best model based on F1 score and AUC.

• Accomplishment: Successfully achieved 93% accuracy using the Gradient Boosting model in identifying the factors associated with happiness.

• 2+ other machine learning projects, 1+ other data wrangling project in Python

• Tableau Dashboard-1
  Purpose: Creating a dashboard in Tableau for visualizing pharmaceutical industry information as required by the management.
  Workings: Collecting data, cleaning data, creating table for Tableau connection, analysing the data as per requirements, and showing the analysis by using bar chart, bubble chart, and pie chart. 
  Accomplishment: Assisting the management in taking quick decisions by looking at the dashboard.

• 1 other Tableau dashboard 9+ other Tableau charts 

• Forecasting of Pharmaceutical Sales
  Purpose: To forecast the sales of Pharmaceutical Company "X" for the next 24 months by analyzing seasonality using R programming.
  Workings: Gathering 111 months of sales data, converting it into a time series, plotting ACF and PACF, using ggsubseries plot to identify seasonality, performing Box Cox transformation and taking the first difference, selecting the order of ARIMA, fitting ARIMA and ETS models, performing classical and STL decomposition, fitting ARIMA and STL models on decomposed data, selecting the best model, and forecasting sales for the next two years. 

• Accomplishment: Successfully analyzed the seasonality of Pharmaceutical sales in Bangladesh and made accurate sales forecasts for the next 24 months.

• 1+ other time series forecasting of S&P 500 and 3 valuation projects on Square Pharma, Grameenphone and Heidelberg Cement. 

• Power BI Dashboard-1:

      Purpose: Creating a dashboard in Power BI for visualizing business information        as required by the management.

      Workings: Collecting data, cleaning data, creating table for PBI connection,            analysing the data as per requirements and showing the info by using stacked        bar chart, donut chart, line chart, funnel chart, combo chart, and waterfall                chart.

      Accomplishment: Assisting the management in taking quick decisions by                  looking at the dashboard.

• 5+ other Power BI dashboards.

• Analytical Insights on Brands Performance:
  Purpose: Finding out the root causes of BDT 45 million less gross profit generation by top ten brands.
  Workings: Analyzing & visualizing the gross profit trend in tableau, conducting interviews, rating the causes based on impacts, and showing the insights on a spider chart.
  Accomplishment: Execution of action plans by the management to regain lost profits.

• 9+ other Business Analysis reports undertaken for management's requirement.

• Principal Component Analysis:
  Purpose: Achieving the optimum number of principal components from the iris data. This project was undertaken as course assignment.
  Workings: Finding variance co-variance matrix, finding eigen values & eigen vectors, evaluating principal components, and plotting the principal components and their associated variance explanation in scatter plot.
  Accomplishment: Learning of finding the optimum number of principal components and plotting the PCAs in scatter plot. 

• 2+ other multivariate projects regarding regression analysis and econometric analysis. 

• Statistical Inference Analysis on Medical Condition:
  Purpose: Hypothesis testing of mean level of cholesterol and creating 95% confidence interval. This project was undertaken as course assignment.
  Workings: Data manipulation, missing data finding, setting target and independent variables, plotting the correlations using seaborn module, conducting t test, and creating 95% confidence interval.
  Accomplishment: Learning of conducting statistical inference analysis by using python. 

• 1+ other hypothesis testing projects. 

• Detecting Outliers by using Box Plot in SPSS
  Purpose: Creating a tutorial on creating box-and-whisker plot in SPSS to detect outliers in a dataset.
  Workings: Uploading a real-world dataset into SSPSS, creating Box Plot is SPSS from graphboard template chooser, interpreting the result, and recording the tutorial in own voice.
  Accomplishment: Learning of detecting outliers form real world dataset.

• Cleaning Dirty Data in Excel
  Purpose: Cleaning dirty data downloaded from company software and turning it to a cleaned dataset. This project was undertaken as management requirements.
  Workings: Copying & pasting raw data sheet to another sheet, using logical statement to fill data in a single column, value pasting database in another sheet to clear formulas, designing table with the database, cleaning blanks and residuals, using logical statement to categorize customer type, using double v-lookup technique to group overdue ages, using conditional formatting to highlight long aged groups.
  Accomplishment: Saved 2 days of man-day of my department by using advanced logics. 

• 2 other Excel dashboards, and 5+ other excel data cleaning and analysis projects that consists advanced pivot table, graphs and charts, descriptive statistics, and formulas.