
Data is the most crucial thing in today’s digital era, this can be data on population, on investment, on sales, on manufacturing, on risk, on product, on loss or on profit. Every organization take data very important for their decision making. Because data is the backbone for every decision that is made in an organization. The number of data an organization has is voluminous; this simply mean that an organization has too much amount of data at their disposal. Therefore, all activities that has to do with collecting this data, organizing this data, arranging or sorting this data for effective decisions making is the task or job of data analyst.
Learn and Grow by building yourself as a data savvy expert that help businesses to make a well-informed decision for their business growth and innovations.
✔️The field of data science – knowing the various field and connecting the Data Science disciplines.
✔️Knowing the benefits of each discipline in data science.
✔️The popular data science techniques and tools.
✔️Careers in Data Science.
✔️The mathematics in data science i.e. statistics and probability, combinatorics, Bayesian inference, and so many others involved.
✔️Python – variables and data types
✔️Basic python syntax
✔️Python operators, conditional statements, functions, sequences, iterations etc.
✔️Advanced statistical methods in python – talking about linear and multiple linear regression with StatsModels.
✔️Advanced statistical methods in python – talking about linear regression with sklearn and practical examples.
✔️Advanced statistical methods in python – talking about logistic regression, cluster analysis, k-means clustering, k-nearest clustering and other types of clustering.
✔️The Basics of machine learning and natural language processing known as NLP.
✔️Deep learning - you will understand what exactly deep learning is, what are neural networks and different, types of neural networks.
✔️Neural networks - How to build a neural network from scratch using NumPy
✔️Deep learning - using Tensorflow.
✔️Digging deeper into neural networks.
✔️The concepts of preprocessing, overfitting, underfitting, initialization and the rest.
✔️Business cases scenario in Deep Learning.
✔️Software integration
✔️Preprocessing the absenteeism data
✔️How to apply machine learning to create the absenteeism module.
✔️Loading the absenteeism module
✔️Analyzing the predicted outputs using Tableau
✔️Random forest to solve classification problems and regression problem with the help of use cases and examples.
✔️K-nearest neighbor to understand how k-nearest can be used to solve complex classification problems and when to used k-nearest.
✔️Next is the naïve Bayes module which is one of the important used in the Gmail spam detection.
✔️Then we move to support vector machine where you will learn how SVM can be used to draw or hyperplane between different class of data.
✔️Then we talk about the unsupervised learning module where you will see how k-means clustering can be used for clustering and how to perform market basket analysis by using association rule mining
✔️The next module is about reinforcement learning where you will understand the different concepts of reinforce of demonstration.
✔️Then lastly you will learn data science interview questions, where you will understand the important concepts of data science along with a few tips in order to aids interview.
✔️SQL
✔️Installing SQL Server management Studio
✔️How to create tables in SQL
✔️Select + From Statements in SQL
✔️Where Statements in SQL
✔️Group By + Order By Statements in SQL
✔️Inner/Outer Joins | Use Cases in SQL
✔️Unions | Union Operator in SQL
✔️Case Statement | Use Cases in SQL
✔️Having Clauses in SQL
✔️Updating/Deleting Data in SQL
✔️Aliasing in SQL
✔️Partition By in SQL
✔️Common Table Expression in SQL
✔️Temp Table in SQL
✔️String Functions + Use Cases in SQL
✔️Stored Procedures + Use Cases in SQL
✔️Subqueries in SQL
✔️SQL Data Exploration
✔️Data Cleaning in SQL
✔️Pivot Tables in Excel
✔️Formulas in Excel
✔️XLOOPUP in Excel
✔️Conditional Formatting in Excel
✔️Charts & Graphs in Excel
✔️Connect to data source with excel
✔️Data Cleaning in Excel
✔️How to prepare reports in Excel
✔️How to automated a repetitive task in Excel
✔️Some analytical Assumptions or what if analysis.
✔️Data Visualization in Tableau
✔️Data cleaning in Tableau
✔️Connect to data sources with Tableau
✔️Using Calculated Fields and Bins in Tableau
✔️Join in Tableau
✔️Data analysis with Tableau
✔️Data Visualization in Power BI
✔️Power Query in Power BI
✔️How to Create and Manage Relationships in Power BI
✔️DAX in Power BI
✔️How to use Drill Down in Power BI
✔️Conditional Formatting in Power BI
✔️The use of Bins and Lists in Power BI
✔️Connect to web data with Power BI
✔️Clean and transform web data with Power BI
✔️Popular Visualizations in Power BI
✔️Installing Jupyter Notebook/Anaconda
✔️Variables in Python
✔️Data Types in Python
✔️Comparison, Logical, and Membership Operator in Python
✔️If else statements in Python
✔️For Loops in Python
✔️While Loops in Python
✔️Functions in Python
✔️Converting Data types in Python
✔️Datasets
✔️Reading in files in Pandas
✔️Filtering Columns and Rows in Pandas
✔️Indexes in Pandas
✔️Group By and Aggregate Function in Pandas
✔️Merging Dataframes in Pandas
✔️Creating Visualizations using Pandas
✔️Data Cleaning in Pandas
✔️Exploratory Data Analysis in Pandas
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