دوره جامع علم داده Data Scientist

آموزش علم داده:

دوره جامع علم داده Data Scientist

مخاطب آموزش دیتاساینس:

علاقمندان به حوزه علم داده

در انتهای دوره دیتاساینس دانشجویان قادر خواهند بود:

تسلط کامل بر اصول طراحی پایگاه داده رابطه ای بازیابی داده ها با زبان T-SQL در سطح پیشرفته
آشنایی با مفاهیم و معماری هوش تجاری
مصور سازی داده ها و ترسیم نمودارها مختلف
پیاده سازی داشبوردهای مدیریتی با Power BI
و به اشتراک گذاری آن در بستر وب و موبایل کار با زبان پایتون در سطح پیشرفته
روشهای تحلیل داده را دقیق یاد گرفته و بتوانند آن‌ها را پیاده سازی کنند.
از کتابخانه های پایتون برای تحلیل داده استفاده کنند.

سرفصل دوره علم داده:

Introduction of Data Science

SQL Server
•Introduction to Microsoft SQL Server 2019
•Install SQL Server 2019
•Relational Database Design Concepts
•Normalization Form
•Creating Tables and Declarative Constraints
•Working with SQL Server 2016 Data Types
•Constraints and Rules
•Introduction to T-SQL Querying
•Writing Basic SELECT Statements
•Using Built-In Functions
•Querying Multiple Tables
•Sorting and Filtering Data
•Grouping and Aggregating Data
•Correlated Query
•Using Subqueries
•Using Common Table Expressions
•Recursive CTE
•Using Set Operators
•Using Windows Ranking, Offset, and Aggregate Functions
•Pivoting and Grouping Sets
•Using DML to Modify Data
•Merge Statement
•Executing Stored Procedures
•Views
•Table-Value and Scalar Function
•Trigger
•Programming with T-SQL
•Implementing Error Handling
•Implementing Transaction
•Row Store and Column Store Index
•Final Project

Power BI
•Introduction Business Intelligence
•Microsoft BI Architecture
•Introduction Self-Service BI
•Install Power BI Desktop
•Data Extraction from CSV, Excel, SQL Server Database, Web Content
•Star and Snowflake Schema
•Import and Direct Query Modes
•Introduction Power Query Editor
•Data Types
•Append Queries and Merge Queries
•Hierarchy
•Introduction to Reports and Visualization Types
•Custom Visuals
•Filters in Power BI
•Slicers in Power BI
•Table and Matrix
•Column and Bar Chart
•Pie chart and Doughnut charts
•Conditional Formatting on Visuals
•Card and Multi-Row Card
•Phone layout
•Treemap
•Combo Chart
•Line chart, Area chart, and Stacked area chart
•Gauge chart
•KPI
•Drill through
•Map and Filled map
•Offline Map ( Synoptic Panel )
•Waterfall Chart
•Funnel chart
•Buttons and Bookmark
•Scatter Chart
•Introduction to Data Analysis Expression (DAX)
•Creating Calculated Columns, Creating Measures
•Introduction DAX Studio
•DAX Date and Time Functions
•DAX Text Functions
•DAX Logical Functions
•DAX Related Function
•DAX Implicit Versus Explicit Measures
•DAX Statistical Functions
•DAX Filter Functions
•DAX X-factor Functions
•DAX Time Intelligence Functions
•DAX Rank Functions
•Persian DimDate
•Tables and Parameters
•Install Power BI Report Server
•Publish Report
•Auto Refresh
•Authorization in Power BI Report Server
•Row-Level Security
•Branding
•Final Project

Python
•Introducing Python
•Python’s applications
•Install and run Python
•Install IDE (PyCharm, Visual Studio Code )
•I/O
•Types
•Variables
•Operators
•Functions
•Conditional
•Loops
•Built-in Functions
•List, Tuple and Dictionary
•Define Function
•Object Oriented Concepts
•Classes
•Fields and methods
•Inheritance
•Override
•Design Patterns
•Using libraries
•Read and write text files
•CSV and Excel File Library (csv, openpyxl)
•Testing with unit test
•GUI with tkinter
•Database Programming with sqlite3 and pyodbc
•Recursive functions
•Exception
•Lambda expressions
•Reflection
•Closure
•Regular expression
•Generators
•Threading & Multiprocessing
•Queue, Stack, Linked list and Tree
•Final Project

Data Science with Python
•Install Anaconda
•Introduction of Python Data Science Libraries
•Data Mining Process
•CRISP
•Data Preparation
•Introduction Pandas
•DataFrames
•Data type conversions using pandas
•Working with String and Dates using pandas
•Dealing with missing data using pandas
•Groupby and aggregations
•Merging (Merge, Join) and concatenating (Concat) dataframes
•Mapping variables into groups
•Plotting with pandas
•Correlations and statistical functions
•Introduction Numpy
•Array and Features
•Array’s Operators
•Numpy Functions
•Indexing and Slicing
•Using Numpy in Linear Algebra
•Introduction to Matplolip
•Graphs
•Bar Graph
•Scatter Graph
•Using Text
•Annotation in Graph
•Scatter plot and Categorical plot
•Histograms
•Pyplot
•Pyplot Tex
•Barh and Fill
•Pcolormesh and Pathpatch
•Streamplot
•Pie Chart
•Table
•Log and Polar
•Customizing Plot
•Customizing Styles
•GridSpec
•3D Line and Bar
•Transformation
•Introduction of Scikit-Learn
•KNN
•Linear Regression
•Logistic Regression
•Clustering
•Linear SVM
•Nave Bayes
•Decision Trees
•Neural Networks
•TensorFlow
•Final Project

جزئیات سرفصل آموزشی دوره جامع علم داده را از اینجا دریافت کنید: