Why it is important to start from basics to learn data science ?

Why it is important to start from basics to learn data science ?
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Hey there. Welcome to Statssy!! Today I will explain you what is the best way to learn data science and what should you focus on to stay motivated.

For those stepping onto the path of a burgeoning career in data science, the landscape may seem daunting. The field itself is rapidly evolving, and the internet is filled with advice suggesting the immediate mastery of advanced tools and techniques. As a newcomer to this field, you might wonder if you should be honing your skills in Python, R, or maybe dive right into machine learning algorithms. The truth, however, may surprise you.

To give you an idea, Microsoft has been leading the way for data analysis. Just look at their products

Tool NamePurposeKey Features
ExcelSpreadsheet AnalysisPivot Tables, Formulas, Data Visualization
Power BIBusiness IntelligenceDashboards, Data Modeling, Real-time Analytics
SQL ServerDatabase ManagementQuery Optimization, Data Warehousing, Security
Azure Data LakeBig Data StorageScalability, Data Lake Analytics, Integration with Azure Services
Azure Machine LearningMachine Learning PlatformAutomated ML, Custom Models, Deployment
Azure Stream AnalyticsReal-time AnalyticsStream Processing, Event Hub Integration, Real-time Dashboards
Azure Synapse AnalyticsAnalytics ServiceBig Data and Data Warehouse, On-demand Query, Serverless Data Exploration
Azure DatabricksApache Spark-based AnalyticsCollaborative Notebooks, Big Data Processing, ML Libraries
Azure HDInsightCloud-based Apache ServiceHadoop, Spark, Hive, LLAP, Kafka
Microsoft R ServerR-based AnalyticsScalable R Analytics, Pre-built ML Algorithms, Integration with SQL Server
basics to learn data science

The Role of Microsoft Excel in Data Analysis

Contrary to the common perception, the very beginning of your journey in data science doesn’t have to be tied with complex programming languages. For many, the journey begins with a tool that’s ubiquitously available and more powerful than one might give it credit for – Microsoft Excel.

  1. Starting with Excel is practical and useful for most data analytics purposes.
  2. Excel is the most commonly used tool in the business world, making it a great starting point.
  3. Microsoft Excel is often enough to tackle most data-related issues in various organizations.

These points underline the broad application and potency of Excel. Techniques like PivotCharts, process behavior charts, and linear/logistic regression can be executed in Excel, enabling novices to perform powerful data analysis. Furthermore, its familiarity and ease of use make it an excellent tool for illustrating complex data in more digestible formats. Considering the utility and prevalence of Excel, it is not surprising that it is the most commonly used business tool for data analysis.

“A significant portion of data-related issues in organizations can be resolved using Excel.”

Just have a look at this table to see power of Microsoft Excel

FeatureDescriptionExample Use-CaseLimitations or Considerations
FormulasPerform calculations and data manipulation=SUM(A1:A10) to sum values in a rangeSyntax errors can occur
FunctionsPredefined formulas for specific tasks=VLOOKUP() for data lookupRequires understanding of function parameters
Pivot TablesSummarize and analyze dataSummarizing sales data by regionCan be complex to set up
Data Sorting and FilteringOrganize data based on specific criteriaSorting a list of employees by ageLimited to basic conditions
Conditional FormattingVisually highlight cells based on conditionsHighlight cells with values above a certain thresholdMay slow down large spreadsheets
Charts and GraphsVisual representation of dataCreating a bar chart to represent yearly salesLimited customization
Data ValidationRestrict the type of data that can be enteredLimiting a cell to only accept numbers between 1 and 100Can be bypassed
What-If AnalysisExplore different scenariosUsing Goal Seek to find the required sales for a target profitLimited to simple scenarios
SolverSolve optimization problemsMaximizing profit by adjusting variable costsRequires setup
Power QueryImport, clean, and transform data from various sourcesImporting data from a SQL databaseLearning curve involved
SlicersInteractive way to filter data in PivotTablesFiltering a PivotTable to show only a specific product lineOnly works with PivotTables
TablesStructured range of data that makes it easier to manageCreating a table for a list of customersLimited to Excel’s row/column limits
Data Bars and Color ScalesUse color to represent data valuesUsing color scales to indicate low, medium, and high valuesVisual only, not data manipulation
Text to ColumnsSplit text into different columnsSplitting full names into first and last namesCan overwrite existing data
Flash FillAutomatically fills in values based on a patternFilling in email addresses based on a list of namesMay not always detect the correct pattern
Forecast SheetPredict future values based on historical dataForecasting next quarter’s salesAssumes linear trend
MacrosAutomate repetitive tasksAutomating a series of steps to clean dataRequires knowledge of VBA
Data ConsolidationCombine data from multiple ranges into a single rangeSumming data from multiple sheets into a summary sheetRequires consistent layout
Named RangesAssign a name to a range of cells for easier referenceNaming a range of cells containing sales data as “SalesData”None
Array FormulasPerform multiple calculations on one or more items in an array=SUM(A1:A3 * B1:B3) to sum the product of two rangesComplex syntax
basics to learn data science

Still not convinced???? No worries lets see how excel is used across the job roles

Use of Microsoft Excel in different domains

Job RoleUse Cases of Excel for Data AnalysisExample Tasks
Financial AnalystBudgeting, Forecasting, Financial ModelingCreating financial models to evaluate investment opportunities
Marketing ManagerCampaign Analysis, Customer SegmentationAnalyzing the ROI of different marketing campaigns
Sales ManagerSales Tracking, Revenue ForecastingUsing PivotTables to track sales performance by region and team
HR ManagerEmployee Data Management, Salary AnalysisUsing Excel to analyze employee satisfaction surveys
Operations ManagerInventory Management, Process OptimizationCreating dashboards to monitor key performance indicators (KPIs)
Data AnalystData Cleaning, Statistical AnalysisUsing Power Query to clean and transform raw data
Project ManagerProject Scheduling, Resource AllocationUsing Gantt charts to visualize project timelines
Supply Chain ManagerLogistics Optimization, Cost AnalysisAnalyzing shipping costs and times to optimize logistics
Customer Service ManagerCustomer Feedback Analysis, Performance MetricsUsing Conditional Formatting to highlight areas needing improvement based on customer feedback
IT ManagerNetwork Monitoring, Security AnalysisUsing Excel to analyze server uptime and downtime
Research and DevelopmentExperimental Data Analysis, Trend IdentificationUsing Excel’s statistical functions to analyze R&D data
Quality AssuranceDefect Tracking, Process ImprovementUsing Excel to log and analyze product defects
Compliance OfficerRegulatory Reporting, Risk AssessmentUsing Excel to prepare reports that comply with industry regulations
Business AnalystBusiness Intelligence, Data VisualizationCreating interactive dashboards using Excel’s Power BI features
Product ManagerProduct Performance Metrics, Market AnalysisUsing Excel to track product usage metrics and identify market trends
Executive LeadershipOverall Business Analysis, Strategic PlanningUsing Excel to analyze company-wide data for strategic decision-making
basics to learn data science

The Importance of Mastering the Basics

The allure of advanced tools and cutting-edge technologies can often overshadow the importance of mastering fundamental concepts. It’s paramount for freshers in the field of data science to recognize the value of a strong foundational understanding.

  1. Learning advanced technologies should follow a firm grasp of the basics.
  2. A ‘crawl, walk, run’ approach is beneficial when learning and implementing data analytics techniques.

This concept mirrors the approach used in many other areas of study. Before you learn to run, you must first learn to walk. In data analytics, running might represent deploying machine learning models or performing complex statistical analysis with Python or R, but walking symbolizes mastering Excel and understanding the essence of data analysis.

Many real-world examples have shown that it’s often the basics that triumph over complexity. For instance, basic techniques like regressions can yield significant insights and often amaze many organizations. As a newcomer, you would be surprised at the powerful insights that can be drawn from these basic methods.

“It’s not necessary to start with programming languages like Python or R, but they can be useful as you progress.”

The Hype vs Reality in Data Science

The data science field is not immune to hype. Buzzwords like ‘Artificial Intelligence’, ‘Machine Learning’, and ‘Deep Learning’ are often thrown around, and the fear of missing out (FOMO) can pressure new data scientists into chasing these advanced technologies.

  1. The fear of missing out (FOMO) on the latest technologies can be misleading and lead to ineffective learning paths.
  2. In reality, most organizations are still far from implementing the latest analytics breakthroughs.

Despite the noise around these advanced techniques, it’s essential to understand that most organizations are still far from using such advanced analytics technologies. Indeed, the high failure rates associated with these techniques further emphasize the necessity of a firm grasp on the basics.

“High project failure rates are associated with the implementation of advanced technologies like machine learning and AI.”

Remember, the goal of data science is not to use the most advanced technologies but to draw actionable insights from data.

And that often begins with a firm grasp of fundamental techniques and tools.

In the next section of the article, we’ll explore the limitations of Excel and when it’s time to transition to more advanced tools like SQL, R, or Python. We’ll also discuss how to tailor your analytics strategy to your organization and take a look at the future of data science. Stay tuned!

Transitioning from Excel to Advanced Tools

Excel is a formidable tool for data analysis, but it has its limitations. As datasets grow in size and complexity, you may encounter issues with data handling, performance, and reproducibility. Therefore, it’s crucial to recognize when Excel’s utility is reaching its zenith, and it’s time to transition to more advanced tools.

  1. Recognizing the limitations of Excel is essential to know when it’s time to switch to other tools.
  2. SQL and R can be learned easily after mastering Excel, offering ways to scale beyond Excel’s limits.
  3. Python might be an easier transition from Excel than R, due to Python’s easier learning curve and general usefulness.

The logical next steps after Excel are often SQL and R. SQL is a language designed for managing and manipulating structured data, while R is a language and environment for statistical computing and graphics. Both offer a means to scale beyond the capabilities of Excel.

Python is another powerful tool for data analysis. It’s generally considered easier to learn than R and is known for its general usefulness. Furthermore, Python has extensive libraries for data manipulation, analysis, and machine learning, making it an excellent choice for data scientists looking to take their skills to the next level.

“Coding can be a helpful skill in data collection and reproducibility.”

Learning to code can seem intimidating, but it opens up a new world of possibilities. It can automate repetitive tasks, access and manipulate large datasets, create reproducible analyses, and much more.

Tailoring Your Analytics Strategy to Your Organization

Not all organizations have the same analytics capabilities. Some companies may have robust data infrastructures capable of supporting advanced analytics technologies, while others may be better off sticking to the basics.

  1. Understanding the cost versus benefits of different implementations and solutions is crucial.
  2. Analytics capabilities in organizations often vary, and what works for one may not work for another.
  3. Advanced technologies are often suitable only for large businesses that can afford the risks.

“Not every company needs to implement cutting-edge analytics technologies to make effective data-driven decisions.”

When planning your analytics strategy, it’s crucial to consider the specific needs and capabilities of your organization. While advanced technologies might seem appealing, they may not be suitable for all businesses due to their risk/reward profile. For example, machine learning models can provide powerful insights but can also be resource-intensive to develop and maintain. In contrast, simpler techniques like regression or decision trees might yield almost as valuable insights, with a fraction of the complexity and risk.

The Future of Data Science

The field of data science is evolving rapidly, with new technologies and techniques emerging regularly. However, the importance of fundamental skills will continue to be crucial.

  1. Solid foundational skills in data analytics are often more valuable than fancy, advanced techniques.
  2. The potential role of advanced technologies in organizations that have the basics in place.

“Advanced technology without the basics in place can harm more than it helps.”

Despite the rapid pace of change, the foundational tools and techniques of data science will continue to be of immense value. Even as more organizations begin to adopt advanced analytics technologies, the basics will remain critical.

In the final part of the article, we’ll summarize the key takeaways and provide some concluding thoughts on the importance of mastering the basics in the evolving field of data science. We’ll also provide some final advice for those new to the field. Stay tuned!


The field of data science may seem overwhelming to beginners, with a plethora of tools and technologies to learn. The relentless hype around advanced tools and techniques can often lead freshers to believe that they need to learn complex methodologies to be successful in their data science journey. However, as we have seen in this article, the reality is quite different.

Summary of Key Points

Here is a quick recap of the key points we discussed:

  • Microsoft Excel is a powerful tool for data analysis, and it can handle a significant portion of data-related issues in various organizations.
  • Fundamental techniques like PivotCharts, process behavior charts, and linear/logistic regression in Excel can yield powerful insights.
  • Understanding the basics is paramount before jumping into advanced tools and techniques. A ‘crawl, walk, run’ approach can be beneficial when learning and implementing data analytics techniques.
  • Advanced technologies like machine learning and AI come with a risk/reward profile that may not be suitable for all businesses. They also have a high project failure rate associated with their implementation.
  • Recognizing when to transition from Excel to advanced tools like SQL, R, or Python is crucial in your data science journey. Python is generally considered easier to learn than R.
  • Tailoring your analytics strategy to your organization’s specific needs and capabilities is essential.

“Master the basics, regardless of the ongoing hype around advanced tools.”

We hope that this article has given you a clearer understanding of the field of data science. While it’s easy to get caught up in the hype around the latest tools and technologies, remember that mastering the basics is the key to success in this field.

We encourage you to share your thoughts on this topic. Have you recently started your data science journey? What tools and techniques are you finding most useful? We would love to hear about your experiences!

Here’s to your success in the exciting world of data science!

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