Data Analytics using Google Gemini with Excel and Python
Enrol today for this free Data Analytics using Google Bard with Excel and Python. Elevate your expertise in this field with guidance from our seasoned experts. Get started now!
Skills you’ll Learn
About this Free Certificate Course
The course begins with an Introduction to Data Analysis using Python and Bard, where you'll learn the basics of Python programming and how to utilize Google Bard for data analysis. Next, EDA using Python and Bard dives deeper into exploratory data analysis techniques, enabling you to extract meaningful insights from your datasets. In Data Modeling for Regression using Python and Bard, we guide you through the process of building regression models to make predictions and understand relationships within your data. The course also covers Introduction to Data Analysis using Excel and Bard, where you'll harness the capabilities of Excel alongside Google Bard for data analysis. EDA using Excel and Bard takes you through further exploratory data analysis techniques in Excel, making you proficient in both tools. Whether you're a beginner looking to enter the world of data analysis or an experienced analyst seeking to expand your skills, this course provides you with the knowledge and expertise needed to succeed.
Course Outline
This module will teach you the fundamentals of data analysis using Bard. You will learn how to import data from a variety of sources, handle missing values, and generate summary statistics. You will also learn how to use Bard to explore your data and identify patterns and trends.
This module will teach you how to explore univariate and bivariate analysis using a variety of charts. You will learn how to use histograms, heatmaps, to visualize the distribution of data, identify patterns and trends, and explore the relationships between variables.
This module will introduce you to the basics of regression modeling using Python and Bard. You will learn how to fit a regression model to data, evaluate its performance, and use Bard to implement a regression model.
This module will teach you the fundamentals of data analysis using Excel and Bard. You will learn how to import data, handle missing values and duplicate values, and generate summary statistics.
This module will teach you how to use Excel commands to explore univariate and bivariate analysis and understand a variety of charts, including Scatter plot, histograms, and pivot tables. You will also learn how to apply various prompts using Bard to automate your data analysis tasks.
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Frequently Asked Questions
What are the prerequisites required to learn this Free Data Analytics using Google Bard Course?
You do not need any prior knowledge to learn this Data Analytics using Google Bard Course.
How long does it take to complete this Free Data Analytics using Google Bard Course?
Free Data Analytics using Google Bard Course is a 1.5 hour long course, but it is self-paced. Once you enrol, you can take your own time to complete the course.
Will I have lifetime access to the free course?
Yes, once you enrol in the course, you will have lifetime access to any of the Great Learning Academy’s free courses. You can log in and learn whenever you want to.
Will I get a certificate after completing this Free Data Analytics using Google Bard Course?
Yes, you will get a certificate of completion after completing all the modules and cracking the assessment.
How much does this Data Analytics using Google Bard Course cost?
It is an entirely free course from Great Learning Academy.
Popular Upskilling Programs
Data Analytics using Google Bard with Excel and Python
Data Analytics is a field that involves the use of statistical and computational methods to extract insights from data. BARD is an effective tool that can be used for web application development and deployment, feature engineering, model selection, hyperparameter tuning, exploratory data analysis, and project planning.
To leverage BARD for Data Analytics, we can follow the steps below:
Project Planning: The planning phase is a crucial step in every project, as it sets the foundation for its success. During this phase, we carefully analyze the available resources and objectives and develop a project plan that outlines the steps required to achieve our goals. To create a project outline, we can craft a detailed prompt for BARD that includes all relevant information about the development of our project.
Data Preprocessing: Data preprocessing is the process of cleaning and transforming raw data into a format that can be easily analyzed. BARD can be used to preprocess data by performing tasks such as data cleaning, normalization, and feature scaling.
Exploratory Data Analysis: Exploratory Data Analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics using visual methods. BARD can be used to perform EDA by generating visualizations such as histograms, scatter plots, and heat maps.
Feature Engineering: Feature engineering is the process of selecting and transforming variables in a dataset to improve the performance of machine learning models. BARD can be used to perform feature engineering by generating new features from existing ones or selecting relevant features using techniques such as Principal Component Analysis (PCA).
Model Selection: Model selection is the process of choosing the best machine learning model for a given problem. BARD can be used to perform model selection by comparing the performance of different models on a validation set.
Hyperparameter Tuning: Hyperparameter tuning is the process of selecting the optimal hyperparameters for a machine learning model. BARD can be used to perform hyperparameter tuning by searching over a range of hyperparameters using techniques such as grid search or random search.
Model Validation: Model validation is the process of evaluating the performance of a machine learning model on an independent test set. BARD can be used to perform model validation by generating performance metrics such as accuracy, precision, recall, and F1 score.
Building and Deployment of Web Application: Finally, BARD can be used to build and deploy web applications that use machine learning models for prediction tasks.
BARD is a strong tool that may be utilized for Data Analytics projects from beginning to end, in conclusion, by leveraging its capabilities for project planning, data preprocessing, exploratory data analysis, feature engineering, model selection, hyperparameter tuning, model validation, and building and deployment of web applications, we can extract insights from data more efficiently and effectively.