What data analysis techniques should you learn to stay competitive in the job market?

Knowing how to present aggregations or learnings from data in proper and subtle representation is essential when narrating the data story to non technical stakeholders. This helps them understand your point, which makes it easy for you to build on their understanding while equipping these stakeholders with actionable steps. However, rather than focus on specific tools (noob mistake), focus on the ability to represent data using standard data visual modelling techniques (normal, poison, scatter plot etc), after learning these apply appropriate colouring to your visuals which will aid the understanding of everyone of your target audience.

Data visualization is incredibly valuable for businesses when it comes to making critical decisions. It not only helps in presenting data in a way that tells a compelling story but also plays a crucial role in predicting what a business and its market might need. Let me illustrate this with a recent experience: We used data visualization to present a story based on Recency, Frequency, and Monetary (RFM) analysis of our customers' buying habits. This not only engaged our customers with new offers but also revealed historical patterns. This goes to show how data visualization doesn't just help with past data but also anticipates what a business and its market will require in the future.

Knowing your data is so valuable for enabling amazing and easy visualisations in Power BI. So definitely start with that: 👍🏻know the different data types 👍🏻in Power Query (free tool within Excel and Power BI) explicitly assign the right data type to each data field 👍🏻learn the principles of a Star Schema data model, e.g. primary keys, foreign keys, facts Vs dimensions 👍🏻understand the grain of your fact tables, e.g. what does a new additional row represent Practice will help you get these things right (learning from doing) and getting it right makes it so much easier to create dashboard in Power BI. And great data model makes it so much easier to learn and write great DAX measures, tables and columns. Have fun practicing!📊📉📈

Knowing the audience will help us choose the correct report, dashboard, and storytelling. By building customized dashboards, we are satisfying stakeholders' needs, allowing them to establish accurate Key Performance Indicators (KPIs) and monitor business goals.

Steve Jobs once said, "The one who masters the art of story-telling is the most powerful person in the world because he is best-skilled to sell his story". I'd go a step further to add story-telling through data as powerful visuals is the most-sought out art of today's corporate world. Few things to keep in mind while putting together a visual story-board: 1. A clean database (Devoid of duplicates, missing values, outliers) 2. Pick and choose the apt visuals that add most weightage (Avoid the clutter; too much information / misinformation are not going to help) 3. Arrange them in a sequence that tells a coherent storyline (Peel the onion). 4. End with an impactful summary that rounds the facts together into a conclusive recommendation.

Focusing on a certain tool is a death sentence for data professional ‘s carrier. Data visualization is the practice of transforming information into a visual context, to make data more comprehensible for end users to extract actional insights from data. Each tool comes with a lot of Pros and Cons such as availability, affordability, etc. It is more important to master the skill and the ability to apply visualization concepts and techniques vs tools to stay current and competitive.

Data visualization is your magic wand in the data world, turning numbers into captivating storiesChoose visuals wisely, keep it simple, use colors purposefully, and let labels guide the way. Craft a compelling data story, consider interactivity, and master your tools. Seek inspiration, embrace feedback, and don't be afraid to experiment. Stay curious, because the world of data visualization is always evolving, and mastering it means making data engaging and accessible to all.

Track every step of your sales process using a data analytics tool so you know the bottlenecks of your funnel quickly! This will maximize adversting spend and increase your ROI and outperform your competition!

Data visualization is a technique used to present data in a visual format, like charts, graphs, and maps. It helps to communicate complex information in a clear and concise way.

Data cleaning is the process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets. It involves handling missing data, detecting outliers, standardizing formats, and ensuring data accuracy. This critical step ensures that data is reliable and ready for meaningful analysis and decision-making.

In my experience, a good way to start data cleaning is by thoroughly examining your dataset and creating a data cleaning plan that outlines the specific issues you need to address. An example of data cleaning is when you have a dataset with inconsistent date formats (e.g., "MM/DD/YYYY" and "YYYY-MM-DD") and you need to standardize them into a single format for consistency. It's important to note that data cleaning is an iterative process. You may need to revisit and revise your data cleaning steps as you uncover new issues during the analysis. Finally, Understanding datacleaning methods, standards, &techniques such as outlier detection, handling missing data, & dealing with inconsistent data entry is essential for effective data quality.

Data cleaning is a very important step in any analytics process. However, it is very important to understand the data domain\filed to have full pictures. For example, in some cases we need to remove outliers yet in other domain we are looking for the outliers. Also it is important to understand how the data was collected to decide how to deal with formatting, standardization and missing data.

Data visualization helps summarize huge chunks of data into consumable portions. Having skills across the board is paramount. For instance, in a social research project, I find Excel as the best go-to tool for analyzing project data and creating dashboards. For business analytics, having skills in any BI tools(PowerBI, Tableau, etc) is paramount. Old good Excel comes in handy too. Knowing what tool to use and when to use it is key. Also, having exemplary storytelling skills goes hand in hand with data visualization skills. You want to be able to explain the visuals in a compelling and easy manner that any non-technical person will understand.

Two simple ways for data cleaning: 1. Remove Duplicates: Identify and eliminate duplicate records from your dataset. Duplicates can distort analysis and skew results. E.g. Python- Use the drop_duplicates() method in pandas to remove duplicates from a DataFrame. SQL - Use the DISTINCT clause or GROUP BY to identify and remove duplicates. 2. Handling Missing Values: Identify and address missing values in your dataset. Missing data can lead to biased results and errors in analysis. E.g. Python - Use the isna() or isnull() methods in pandas to detect missing values and then use fillna() or dropna() to handle them. SQL- Use the IS NULL condition to filter rows with missing values and then decide whether to replace or exclude those rows..

Data cleaning, also known as data cleansing or data scrubbing, is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It involves tasks such as handling missing values, removing duplicate entries, correcting formatting issues, and resolving inconsistencies in data. The goal of data cleaning is to ensure that the dataset is accurate, reliable, and suitable for analysis and decision-making. By cleaning the data, we can improve the quality and integrity of the dataset, leading to more accurate and meaningful insights.

Data manipulation means changing, sorting, and organizing data to make it useful. You can filter, sort, and do math with it. You can also combine data from different places, group it, or fix errors. It's like arranging puzzle pieces to see the big picture. This helps us analyze and understand data better. We use tools like Excel, SQL, or Python to do this stuff.

A great way to begin with data manipulation is by first defining your data transformation goals. What specific insights or variables do you need from your data? An example of data manipulation is when you merge two datasets based on a common key, such as customer IDs, to combine information from different sources into a single dataset for analysis. It's worth emphasizing that data manipulation involves not only creating new variables but also cleaning and preparing the data before applying transformations to ensure accuracy and consistency.

Data manipulation refers to the process of altering, transforming, or modifying data to extract useful information or prepare it for analysis. It involves various operations such as filtering, sorting, aggregating, joining, and transforming data to meet specific requirements. Data manipulation allows us to organize and reshape data in a way that is more suitable for analysis or to derive new insights. It can involve tasks like selecting specific columns or rows, calculating new variables, merging datasets, or summarizing data. Overall, data manipulation is an essential step in the data analysis process, enabling us to manipulate and shape data to uncover patterns, trends, and relationships.

Data modeling plays a crucial role in data analytics by providing a structured framework for organizing and representing data. It helps analysts understand data relationships, identify patterns, and uncover insights. This process ensures that data is efficiently processed and analyzed, leading to informed decision-making and the extraction of valuable information from complex datasets.

Data modelling IS the most important skill to understand (and master) by Data Analysts. Even if you read about new "methodologies" like 'One Big Table', Dimensional modelling concepts will still reign supreme. Learn about Star schema's, Snowflake schema's, relationship types • One to One • One to Many • Many to Many Learn about keys: • Foreign keys • Primary keys • Surrogate keys Read more about the Kimball methodology- and continously apply these. Benefits of Data modelling: • Improved report performance • Reduced data redundancy

-The first step in data modeling is to clearly define your problem or research question. What are you trying to predict or understand with your data model? - An example of data modeling is building a linear regression model to predict a house's price based on features like square footage, number of bedrooms, and location. - It's important to emphasize the importance of data preprocessing before modeling. Cleaning, transforming, and engineering features are crucial steps to ensure the quality of your input data for modeling.

Data modeling involves defining the structure, constraints, and rules that govern how data is organized and stored. Data models provide a blueprint for designing databases, software systems, or any other data-intensive applications. They help in understanding the data requirements, identifying entities, attributes, and relationships, and ensuring data integrity and consistency.

In data analysis, data communications refer to how you are going to communicate your finding, result, etc. Each data analyst needs to master to translate complex concepts into crystal-clear recommendations. That would need a full understating of your clients\end user to understand the business process and determine how your finding will impact the business process.

Data communication refers to the transmission and exchange of data between different systems, devices, or users. It involves the transfer of data through various communication channels, such as networks or protocols, to enable sharing and collaboration. Data communication can occur in various forms, including text, files, images, or real-time streaming. It plays a crucial role in enabling data sharing, collaboration, and integration across different platforms, systems, or organizations.

I believe that one of the primary responsibilities for data analysts is mastering the use of predictive and prescriptive analytics. It's not just about assessing past performance; it's equally crucial to anticipate future outcomes. Additionally, prescriptive analytics plays a vital role in providing actionable recommendations based on data. For example, when goods have been shipped, it can suggest sending an invoice to the customer, especially if this action hasn't been automatically executed.

Remember that ultimately you want your analytics consumer to make a data-driven decision with your output. No matter how well crafted your analysis or dashboard is, if you aren’t directly addressing a decision that they need to make, your output may go unused.

Effective data visualization is a powerful tool for conveying complex information. One key aspect is choosing the right type of chart or graph for your data. For instance, bar charts are great for comparing categories, line charts for showing trends over time, and scatter plots for identifying relationships between variables. Always consider your audience and the message you want to convey when selecting a visualization type. Good data visualization simplifies complex data, making it more accessible and understandable.

To stay competitive in the job market across all industries, it's essential to have proficiency in both descriptive and predictive analysis techniques. Descriptive analysis provides insights into past behaviors and helps in understanding the present state, while predictive analysis uses historical data to forecast future outcomes. Familiarity with these foundational techniques will ensure you're well-equipped to handle a broad range of data-related challenges.

Data visualization is the most simplest and powerful tool that we all can use as data analysis techniques I order to breakdown complex problems into understandable state. However advanced techniques like getting familiar with Machine learning algorithms like decision tree, random forests, Logistic regression and neural networks can help you build predictive models and solve complex business problems.