Data Analytics Using Python
Are you looking for the best Data Analytics with Python training institute in Noida? Data Analytics using Python training classes with live projects are offered by KASYFY in Noida by an expert trainer. Our Data Analytics with Python training program in Noida is tailored to meet the needs of undergraduates, graduates, working professionals, and freelancers. We offer end-to-end Data Analytics training in the Python domain, as well as deeper dives into specific topics to help you build a successful career.
Why Should You Enroll in Our
Python Data Analytics
Training Course in Noida?
We place a premium on innovative ideas, high-quality training, smart classes, 100% job assistance, and opening doors to new possibilities. Our Python Data Analytics Trainees are working all over the country. KASYFY is the No. 1 Data Analytics with Python Course in Noida, with a 100% placement rate. Over 10,000 students have been trained in Data Analytics using Python by certified trainers in Noida.
What Will Our Students Learn During the
Python Data
Analytics
Training Course?
Student support, career services, industry expert mentors, and real-world projects are all available. Counseling on a career path Resolving Doubts in a Timely Manner. Salary Increase by 50%, Career Counseling Case Studies + Tools, and a Certificate.
Why should you learn Python for Data Analytics?
It’s remained a popular choice among data scientists who use it to create Machine Learning applications or to perform other scientific computations. Python Data Analytics Training in Noida reduces development time by half thanks to its easy-to-read syntax and compilation feature, as well as concepts that are simple to grasp. With its built-in debugger, debugging any type of program is a breeze in this language.
It has been ported to Java and.NET virtual machines and runs on every well-known platform, including Windows, Linux/Unix, and Mac OS. Python is an open-source language that anyone can use for free, even for commercial products, thanks to its OSI-approved open source license. Python has emerged as the most popular language for data analytics, with daily search trends indicating that it is the “Next Big Thing” and a must-have for anyone interested in the field.
Why KASYFY?
For our students, KASYFY has a dedicated team of highly experienced trainers who identify, evaluate, implement, and provide the Best Data Analytics Using the Python Training Institute in Noida. Our trainers use a well-defined methodology to help you identify opportunities, develop the best solution, and execute it effectively. To provide the Best Data Analytics Using Python Training in Noida, we have the best trainers from all over the world who are highly qualified and the best in their field.
The Training & Placement cell is dedicated to assisting students in their efforts to find employment and internships in a variety of fields. The placement department collaborates with other departments to mold students to meet the needs of various industries. We have proactive and business-savvy Placement Cells that take pride in having a strong skilled network across a wide range of industries. It works closely with each student to ensure that they are placed with ostensibly multinational corporations within six months of graduation. In Noida, we are the best Data Analytics with Python Training Institute.
- Course Curriculam
- Why Python
- Application areas of python
- Python implementations
- Cpython
- Jython
- Ironpython
- Pypy
- Python versions
- Installing python
- Python interpreter architecture
- Python byte code compiler
- Python virtual machine(pvm)
- Using interactive mode
- Using script mode
- General text editor and command window
- Idle editor and idle shell
- Understanding print() function
- How to compile python program explicitly
- Character set
- Keywords
- Comments
- Variables
- Literals
- Operators
- Reading input from console
- Parsing string to int, float
- If statement
- If else statement
- If elif statement
- If elif else statement
- Nested if statement
- While loop
- For loop
- Nested loops
- Pass, break and continue keywords
- Int, float, complex, bool, nonetype
- Str, list, tuple, range
- Dict, set, frozenset
- What is string
- String representations
- Unicode string
- String functions, methods
- String indexing and slicing
- String formatting
- Creating and accessing lists
- Indexing and slicing lists
- List methods
- Nested lists
- List comprehension
- Creating tuple
- Accessing tuple
- Immutability of tuple
- How to create a set
- Iteration over sets
- Python set methods
- Python frozenset
- Creating a dictionary
- Dictionary methods
- Accessing values from dictionary
- Updating dictionary
- Iterating dictionary
- Dictionary comprehension
- Why modules
- Script v/s module
- Importing module
- Standard v/s third party modules
- Why packages
- Understanding pip utility
- Introduction to file handling
- File modes
- Functions and methods related to file handling
- Understanding with block
- Need of regular expressions
- Re module
- Functions /methods related to regex
- Meta characters & special sequences
- Database Concepts
- What is Database Package?
- Understanding Data Storage
- Relational Database (RDBMS) Concept
- SQL basics
- DML, DDL & DQL
- DDL: create, alter, drop
- SQL constraints:
- Not null, unique
- Sample or population
- Measures of central tendency
- Arithmetic mean
- Harmonic mean
- Geometric mean
- Mode
- Quartile
- First quartile
- Second quartile(median)
- Third quartile
- Standard deviation
- Introduction to probability
- Conditional probability
- Normal distribution
- Uniform distribution
- Exponential distribution
- Right & left skewed distribution
- Random distribution
- Central limit theorem
- Normality test
- Mean test
- T-test
- Z-test
- ANOVA test
- Chi square test
- Correlation and covariance
- Difference between list and numpy array
- Vector and matrix operations
- Array indexing and slicing
- Labeled and structured data
- Series and dataframe objects
- From excel
- From csv
- From html table
- at &iat
- loc&iloc
- head() & tail()
- describe()
- groupby()
- crosstab()
- boolean slicing / query
- Map(), apply()
- Combining data frames
- Adding/removing rows & columns
- Sorting data
- Handling missing values
- Handling duplicacy
- Handling data error
- Scatter plot, lineplot, bar plot
- Histogram, pie chart,
- Jointplot, pairplot, heatmap
- Outlier detection using boxplot
- Customizing common options in Excel
- Absolute and relative cells
- Protecting and un-protecting worksheets and cells
- Writing conditional expressions (using IF)
- Using logical functions (AND, OR, NOT)
- Using lookup and reference functions (VLOOKUP, HLOOKUP, MATCH, INDEX)
- VlookUP with Exact Match, Approximate Match
- Nested VlookUP with Exact Match
- VlookUP with Tables, Dynamic Ranges
- Nested VlookUP with Exact Match
- Using VLookUP to consolidate Data from Multiple Sheets
- Specifying a valid range of values for a cell
- Specifying a list of valid values for a cell
- Specifying custom validations based on formula for a cell
- Designing the structure of a template
- Using templates for standardization of worksheets
- Sorting tables
- Using multiple-level sorting
- Using custom sorting
- Filtering data for selected view (AutoFilter)
- Using advanced filter options
- Creating subtotals
- Multiple-level subtotals
- Creating Pivot tables
- Formatting and customizing Pivot tables
- Using advanced options of Pivot tables
- Pivot charts
- Consolidating data from multiple sheets and files using Pivot tables
- Using external data sources
- Using data consolidation feature to consolidate data
- Show Value As ( % of Row, % of Column, Running Total, Compare with Specific Field)
- Viewing Subtotal under Pivot
- Creating Slicers ( Version 2010 & Above)
- Date and time functions
- Text functions
- Database functions
- Power Functions (CountIf, CountIFS, SumIF, SumIfS)
- Using auto formatting option for worksheets
- Using conditional formatting option for rows, columns and cells
- Relative & Absolute Macros
- Editing Macro’s
- Goal Seek
- Data Tables
- Scenario Manager
- Using Charts
- Formatting Charts
- Using 3D Graphs
- Using Bar and Line Chart together
- Using Secondary Axis in Graphs
- Sharing Charts with PowerPoint / MS Word, Dynamically
- (Data Modified in Excel, Chart would automatically get updated)
- Sparklines, Inline Charts, data Charts
- Overview of all the new features
The Final Assignment would test contains questions to be solved at the end of the Course
- Swap Values, Run Code from a Module, Macro Recorder, Use Relative References,
- FormulaR1C1, Add a Macro to the Toolbar, Macro Security, Protect Macro.
Logical Operators, Select Case, Tax Rates, Mod Operator, Prime Number Checker, Find Second Highest Value, Sum by Color, Delete Blank Cells.
- Tableau – overview
- Tableau – environment setup
- Tableau – get started
- Tableau – navigation
- Tableau – design flow
- Tableau – file types
- Tableau – data types
- Tableau – show me
- Tableau – data terminology
- Tableau – custom data view
- Tableau – data sources
- Tableau – extracting data
- Tableau – fields operations
- Tableau – editing metadata
- Tableau – data joining
- Tableau – data blending
- Tableau – add worksheets
- Tableau – rename worksheet
- Tableau – save & delete worksheet
- Tableau – reorder worksheet
- Tableau – paged workbook
- Tableau – operators
- Tableau – functions
- Tableau – numeric calculations
- Tableau – string calculations
- Tableau – date calculations
- Tableau – table calculations
- Tableau – lod expressions
- Tableau – basic sorting
- Tableau – basic filters
- Tableau – quick filters
- Tableau – context filters
- Tableau – condition filters
- Tableau – top filters
- Tableau – filter operations
- Tableau – bar chart
- Tableau – line chart
- Tableau – pie chart
- Tableau – crosstab
- Tableau – scatter plot
- Tableau – bubble chart
- Tableau – bullet graph
- Tableau – box plot
- Tableau – tree map
- Tableau – bump chart
- Tableau – gantt chart
- Tableau – histogram
- Tableau – motion charts
- Tableau – waterfall charts
- Tableau – dashboard
- One project using python &sql
- One dashboard using tableau
Frequently Asked Questions (FAQ’s)
Anybody with prior domain knowledge in respective fields can get into Python Full Stack.
Different payment options are available suiting your needs. We accept credit cards, debit cards cheque cash , netbanking, money wallets.
With tieup through our job consultancy firm, Kasyfy, we make sure each and every candidate in placed with a top tier firm.
With guaranteed results from last 17 Years, we make you stand out in the market with hands on knowledge and better understanding of current scenarios in the market, in turn helping your to automatically get placed with MNCs.
With approximate range between 2 to 3 months, there are more options like fast track training and online training options are also available. Fees is reasonable according to market and industry standards.
Training Highlights
- Customized course
- Live Projects
- Mock Test
- Multiple Training Modes
- Certified Trainer
- Official Course
- 24*7 Mentoring
