Internship on Business Analytics

Internship in Business Analytics

Eligibility – Pursuing Graduates B.E/ B.Tech

Intermediate

Overview

Business Analytics Internship

Step into the world of data-driven insights and strategic decision-making through Cranes Varsity’s prestigious Business Analytics Internship. Designed for aspiring analysts and data enthusiasts, this program offers an immersive experience in harnessing the power of data. With a curriculum spanning diverse tools and techniques including Python, R, Tableau, SQL, and Excel, interns gain hands-on expertise in data visualization, predictive modeling, and statistical analysis.

Guided by seasoned industry professionals, participants work on live projects mirroring authentic business scenarios, honing their problem-solving skills and analytical acumen. Collaborations with industry partners provide exposure to varied sectors, networking avenues, and a pulse on emerging trends.

Tailored for students pursuing degrees in business, statistics, mathematics, computer science, or any quantitative discipline, as well as individuals passionate about leveraging analytics for impactful decision-making.

Cranes Varsity’s strong industry connections open doors to rewarding career opportunities, empowering interns to translate their newfound expertise into tangible professional success. Seize this unparalleled opportunity to immerse yourself in the dynamic world of business analytics, acquire invaluable experience, and pave your path to a successful career.

Why Choose Our Business Analytics Internship Program?

Embark on a transformative journey into the realm of business analytics with Crane’s Varsity’s unparalleled internship program. At Crane’s, we go beyond conventional learning, offering an immersive experience meticulously crafted by industry experts to align with the pulse of the business world. What sets us apart? It’s our unwavering commitment to providing hands-on, practical exposure that transcends theory. Through live projects and mentorship from seasoned professionals, you’ll not only grasp the intricacies of analytics but also thrive in applying these skills to real-world scenarios. Our program isn’t just about skill acquisition; it’s about fostering innovation, critical thinking, and problem-solving abilities that are paramount in today’s competitive landscape. Furthermore, our emphasis on networking opportunities, flexibility, and the assurance of a recognized certification upon completion ensures that you’re not just learning; you’re propelling yourself toward a promising career. Join Crane’s Varsity and unlock your potential – become a trailblazer in the dynamic realm of business analytics.

Business Analytics Course Content

Day 1: Introduction to Business Analytics

Introduction to Business Analytics

  • Importance of Business Analytics
  • What is data
  • Data and data sources
  • Different data sources in business

Excel Basics

    • Introduction to excel
    • Data entry and basic functions
Day 2: Excel Continued

Excel Continued

  • Excel formulas and functions
  • Using formulas (eg: SUM, AVERAGE)
  • Creating basic charts
  • Data cleaning in excel
  • Data formatting in excel
  • Data cleaning techniques
  • Formatting data for analysis
Day 3: Pivot Table in Excel

Pivot Table in Excel 

  • Data analysis with pivot tables
  • Creating pivot tables
  • Analyzing data with pivot tables
  • Advanced excel functions
  • Using advanced functions(vlookup, hlookup,xlookup)
Day 4: Data visualization

Data visualization

    • Data visualization in excel
    • Different chart types used in excel
    • How to structure data for effective visualization
    • Adding and customizing data labels, tiles, axes
    • Stacked and clustered chart for complex data
    • Creating combination charts
    • 3D charts and surface charts
Day 5: Data Annotations, Styling, Conditional Formatting

Data Annotations, Styling, Conditional Formatting

    • Adding and formatting data labels
    • Using callouts and text boxes to annotate the charts
    • Customizing the appearance of charts
    • Use of themes and templates for better visualization
    • Highlight data points using color scales.
    • Applying conditional formatting to heatmaps
Day 6: Chart Elements, Sparklines

Chart Elements, Sparklines

    • Legends, data tables, trend lines
    • Positioning and formatting chart elements
    • Scrollbars, slicers and drop down lists
    • Creating miniature
    • In-cell charts to visualize trends within a single cell
Day 7: Pivot Charts, Templates

Pivot Charts, Templates

  • Creating dynamic charts
  • Chart changes as the pivot table data changes
  • Chart templates
  • Use of template to save time
  • Template that maintains consistency of the charts

Best practices

      • Avoid chart junks
      • Understanding principles of data-ink ratio
      • Tufte’s principles of chart design
Day 8: Case Studies,  Analysis Tools, Exporting

Case Studies,  Analysis Tools, Exporting

  • Analyzing real world data set
  • Creating meaningful visualization
  • Power query, pivot table
  • Power Pivot
  • Exporting charts as images or pdf’s
  • Embedding excel charts in word documents
  • Embedding charts in powerpoints
Day 9: Automation, Dashboards

Automation, Dashboards

    • Automating the process
    • Creating and updating charts – VBA macros
    • Creating interactive, informative dashboards
    • Creating dashboards with slicers and multiple charts
Day 10: Python for Business Analytics

Python for Business Analytics

    • Introduction to data visualization
    • Data visualization with python
    • Visualization using libraries such as matplotlib
Day 11: Seaborn

Seaborn

  • Introduction to seaborn
  • Various charts in seaborn
  • Analyzing and visualizing data using seaborn
  • Creating visualizations using seaborn
Day 12: Python for data cleaning

Python for data cleaning

  • Statistical analysis in python
  • Descriptive statistics
  • Data wrangling
  • Methods in data cleaning
  • Handling missing data
  • dropna, fillna, ffill, bfill

interpolate

Day 13: Machine Learning

Machine Learning

  • Introduction to machine learning
  • Supervised, unsupervised learning
  • Linear regression
  • Excel vs python for data analysis
  • Pros and cons of using excel and python
Day 14: Power BI

Power BI

  • Introduction to Power BI and it’s components
  • Installing Power BI desktop
  • Collecting data from data sources

Building your first simple report

Day 15: Data transformation

Data transformation

  • Power Query editor
  • Understanding the power query editor
  • Data cleaning and shaping
  • Merging and appending queries
  • Managing query dependencies
Day 16: Data modelling

Data modelling

  • Introduction to data modeling concepts.
  • Creating relationships between tables.
  • Managing and enhancing data models.
  • Measures and calculated column
Day 17: Visualization

Visualization

  • Introduction to visualizations in Power BI.
  • Creating basic visualizations: bar charts, column charts, line charts.
  • Formatting options for visuals.

Interactivity in visuals

Day 18: Visualization continued

Visualization continued

  • Advanced visualizations: scatter plots, tree maps, cards, and more.
  • Custom visuals and marketplace.
  • Drill-through and drill-down.
  • Using bookmarks for storytelling.
Day 19: Data analysis and expressions (DAX)

Data analysis and expressions (DAX)

  • Introduction to DAX
  • Creating simple DAX measures
  • Using DAX functions for basic calculations
  • Aggregations using DAX
  • Filtering using DAX
Day 20: Data analysis and expressions (DAX) continued

Data analysis and expressions (DAX) continued

  • Advanced DAX functions.
  • Time-intelligence functions.
  • Creating calculated tables.
  • Introduction to DAX optimization techniques.
  • Best practices in Power Business Intelligence
Project Presentation & Wrap-Up After Completion

Time will be given for students to prepare and present

  • Participants work on project that integrates skills learned during the program.
  • Presentation of projects and feedback.
  • Q&A session and final evaluation.
  • Recap
  • steps for further learning.

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