PG Program in Data Analytics

This course will introduce you to the world of Python programming language that is widely used in Artificial Intelligence and Machine Learning. We will start with basic ideas before going on to the language's important vocabulary as search phrases, syntax, or sentence building. This course will take you from the basic principles of AI and ML to the crucial ideas with Python, among the most widely used and effective programming languages in the present market. In simple terms, Python is like the English language.

Python is a popular high-level programming language with a simple, easy-to-understand syntax that focuses on readability. This module will guide you through the whole foundations of Python programming, culminating in the execution of your 1st Python program.

Using Jupyter Notebook, you will learn how to use Python for Artificial Intelligence and Machine Learning. We can create and share documents with narrative prose, visualizations, mathematics, and live code using this open-source online tool.

For code reusability and software modularity, functions & packages are used. In this module, you will learn how you can comprehend and use Python functions and packages for AI.

In this module, you will learn how to use Pandas, Matplotlib, NumPy, and Seaborn to explore data sets. These are the most frequently used Python libraries. You'll also find out how to present tons of your data in simple graphs with Python libraries as Seaborn and Matplotlib.

Understanding Data Structures is among the core components in Data Science. Additionally, data structure assists AI and ML in voice & image processing. In this module, you will learn about data structures such as Data Frames, Tuples, Lists, and arrays, & precisely how to implement them in Python.

In this module, you will learn about the words and ideas that are important to Exploratory Data Analysis and Machine Learning. You will study a specific set of tools required to assess and extract meaningful insights from data, from a simple average to the advanced process of finding statistical evidence to support or even reject wild guesses & hypotheses.

Descriptive Statistics is the study of data analysis that involves describing and summarising different data sets. It can be any sample of a world's production or the salaries of employees. This module will teach you how to use Python to learn Descriptive Statistics for Machine Learning.

In this module, you will use Python to study the core ideas of using data for estimating and evaluating hypotheses. You will also learn how you can get the insight of a large population or employees of any company which can't be achieved manually.

Probability is a quantitative tool for examining unpredictability, as the possibility of an event occurring in a random occurrence. The probability of an event occurring because of the occurrence of several other occurrences is recognized as conditional probability. You will learn Probability and Conditional Probability in Python for Machine Learning in this module.

With this module, you will learn how to use Python for Hypothesis Testing in Machine Learning. In Applied Statistics, hypothesis testing is among the crucial steps for conducting experiments based on the observed data.

With this module, you will learn supervised machine learning algorithms, the way they operate, and what applications they can be used for - Classification and Regression.

Linear Regression is one of the most popular Machine Learning algorithms for predictive studies, leading to the very best benefits. It is an algorithm that assumes the dependent and independent variables have a linear connection.

Logistic Regression is one of the most popular machine learning algorithms. It is a fundamental classification technique that uses independent variables to predict binary data like 0 or 1, positive or negative , true or false, etc. In this module, you will learn all of the Logistic Regression concepts that are used in Machine Learning.

k-Nearest Neighbours (Knn) is another widely used Classification algorithm, it is a basic machine learning algorithm for addressing regression and classification problems. With this module, you will learn how to use this algorithm. You will also understand the reason why it is known as the Lazy algorithm. Interesting Right?

Support Vector Machine (SVM) is another important machine learning technique for regression and classification problems. In this module, you will learn how to apply the algorithm into practice and understand several ways of classifying the data.

We explore beyond the limits of supervised standalone models in this Machine Learning online course and then discover a number of ways to address them, for example Ensemble approaches.

The Decision Tree algorithm is an important part of the supervised learning algorithms family. The decision tree approach can be used to resolve regression and classification problems unlike others. By learning simple decision rules inferred from previous data, the goal of using a Decision Tree is constructing a training type that will be used to predict the class or value of the target varying.

Random Forest is a common supervised learning technique. It consists of multiple decision trees on the different subsets of the initial dataset. The average is then calculated to enhance the dataset's prediction accuracy.

When the aim is to decrease the variance of a decision tree classifier, bagging is implemented. The average of all predictions from several trees is used, that is a lot more dependable than a single decision tree classifier.

Boosting is a technique for generating a set of predictions. Learners are taught gradually in this technique, with early learners fitting basic models to the data and consequently analyzing the data for errors.

In this module, you will study what Unsupervised Learning algorithms are, how they operate, and what applications they can be used for - Clustering and Dimensionality Reduction, and so on.

In Machine Learning or even Data Science, K-means clustering is a common unsupervised learning method for managing clustering problems. In this module, you will learn how the algorithm works and how you can use it.

Hierarchical Clustering is a machine learning algorithm for creating a bunch hierarchy or tree-like structure. It is used to group a set of unlabeled datasets into a bunch in a hierarchical framework. This module will help you to use this technique.

PCA is a Dimensional Reduction technique for reducing a model's complexity, like reducing the number of input variables in a predictive model to avoid overfitting. Dimension Reduction PCA is also a well-known ML approach in Python, and this module will cover all that you need to know about this.

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to identify arbitrary-shaped clusters and clusters with sound. You will learn how this algorithm will help us to identify odd ones out from the group.

• Overview_Excel
• How To Write Down Data In Excel
• Basic Formula

• Overview_Function_ppt
• Upper_lower_Function(All Basic Function Write Down)
• Simple_if_Function,Sum_if(Simple_Conditional Formating)
• Text_column and Multiple _if
• Sum_if and Sum_ifs
• Average_if and Average_ifs
• Use Referances

• LOOKUP
• OverView Of Vlookup, Index, Hlookup
• HLOOKUP, COMMENT
• vlookup + match
• vlookup
• VLOOKUP WITH WILDCARD
• VLOOKUP+CHOOSE+DSUM+DCOUNT
• MATCH
• INDEX_MATCH
• IMAGE_LOOKUP

• DATE_FUNCTION
• DATE_DIFF
• work_days and Network_days Function
• CONSOLIDATE_REMOVE DUPLICATE
• DATA TABLE
• DATA_VALIDATION
• FLASH_FILL+FREEZE PEN
• GOAL SEEK AND SCEANIRYO
• INDIRET_DEP
• PIVOT_TABLE PPT
• TIME
• PIVOT_TABLE PRATICAL1
• PIVOT_TABLE PRATICAL2
• DOUBLE_VLOOKUP
• FINANCIAL_FUNCTION
• PIVOT_TABLE
• PIVOT_TABLE_OPTION
• PIVOT_TABLE_+REPORTFILTER
• CHARTS_USING CONDITIONAL FORMATING
• SPARKLINE
• PIVOT_CHARTS
• filter_pivottable
• SUB_TOTAL
• VISIBLE_CELLS_ONLY
• PIVOT_TABLE_GROUP
• GROUPING+CHARTS
• WORKING WITH _OBJECTS
• ONE_PIVOTTABLE WITH TWO SLICERS
• DASHBOARD

• MACRO
• MACRO+CODING
• MACRO EXAMPLES
• CUSTOM_FORMATING
• ADVANCE_FILTER
• PIVOT_TABLE_NEW
• MACRO_PPT
• MACRO_DATABASE
• ADVANCEFILTER+MACRO
• RELATIVE_REFRENCES
• MACRO_BASIC
• form_contro
• Option_control(checkbox+option)
• DIFFERENCE_BETWEEN(ACTIVEX+FORM CONTROL)
• COMBOBOX VALUE
• FORM WITHOUT VBA
• RELATIONSHIP_PIVOTTABLE
• PASTE SPECIAL

Tableau is a visual platform for business intelligence and analytics which allows users to monitor, comprehend, observe, and make choices with a range of data. It allows you to produce any kind of graph, plot, or chart without scripting.

You can learn how to handle various types of data, plot, analyze, sort, and summarise the entire data.

Tableau Reports are a built-in feature of Tableau that allows users to display data from different sources into reports to better comprehend how far they have progressed toward their goals, better understand their customers' needs, and forecast future plans.

You will use calculated fields to generate new data from existing data in your data source. When you build a calculated field in your data source, you are efficient in including a new field (or column) whose values or members are determined by a computation that you control.

Table computations are modifications that can be applied to the data in a display. They are a kind of calculated field that works with Tableau's local data based on what is already in view.

Parameters let you change a reference line, band, or box dynamically. For instance, reference a parameter rather than a reference line at a defined position on the axis. The reference line could be moved using parameter control.

An interactive dashboard allows you to dive down and filter operational data, allowing you to see data from multiple angles or in more depth. Dashboards allow data-driven business choices by providing a simplified and clear overview of overall business information.

A narrative in Tableau is a set of visuals that work together to present data. You can use tales to convey a data story, offer context, display how steps affect results or make a strong argument.

In this module, you will learn to join the tables, data blending and make connections, etc.

In this module, you will learn to share Workbooks, Publish to Reader/PDF and Publish to Tableau Server and share on the web.