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- Overview of Python
- The Companies using Python
- Different Applications where Python is Used
- Discuss Python Scripts on UNIX/Windows
- Values, Types, Variables
- Operands and Expressions
- Conditional Statements
- Loops
- Command Line Arguments
- Writing to the Screen
- What is Data Science?
- What does Data Science involve?
- Era of Data Science
- Business Intelligence vs Data Science
- Life cycle of Data Science
- Tools of Data Science
Data Science with Python Certification Course
Quick Facts
| particular | details | ||||
|---|---|---|---|---|---|
|
Medium of instructions
English
|
Mode of learning
Self study, Virtual Classroom
|
Mode of Delivery
Video and Text Based
|
Frequency of Classes
Weekends
|
Course overview
Data Science with Python Certification Course is being provided by Edureka for the learners who are keen to strengthen their base of data science concepts. Edureka is an educational platform, which provides the learners with a chance to pursue their courses through live online courses.
The participants of this course will not only learn the fundamentals of Statistics, Python and Machine Learning but will also understand the application of python in Data Science. This course is a step by step guide to Data science and python with extensive hands on.
They will learn from the basics of Statistics such as median, mean, and mode to exploring features such as Regression, Data Analysis, classification, clustering, cross validation, naive Bayes, label encoding, random forests, support vector machines and decision trees with examples and exercises to help the participants understand better.
They will be taught reinforcement learning, an important aspect of Artificial Intelligence and application of Machine Learning Algorithms. This course will cover basic as well as advanced concepts of Python like writing Python scripts, file operations, the sequence in Python and the usage of libraries like pandas and Numpy.
The highlights
- Live sessions by instructors
- Course certificate
- 24 x 7 Expert Support
- Course access for lifetime
Program offerings
- Assignments
- Community forums
- Real life case studies
Course and certificate fees
Fees information
₹ 19,795 ₹21,995
Fee details for Data Science with Python Certification Course
| Head | Amount in INR |
| Original price | Rs. 21,995 |
| Discounted price | Rs. 19,795 |
*No Cost EMI starts at Rs. 6,599 / month
certificate availability
Yes
certificate providing authority
Edureka +1 more
Who it is for
The Data Science with Python Certification Course is suitable for:
- Programmers
- Developers
- Technical Leads
- Architects
- Developers aspiring to be a ‘Machine Learning Engineer'
- Analytics Managers leading a team of analysts
- Business Analysts who wish to understand Machine Learning (ML) Techniques
- Information Architects who aspire to gain expertise in Predictive Analytics, 'Python' professionals keen on designing automatic predictive models.
Eligibility criteria
Education
Candidates interested in pursuing this course should have a basic understanding of Computer Programming Languages. Fundamentals of Data Analysis practiced over any of the data analysis tools like SAS/R will be beneficial. However, you will be given “Python Statistics for Data Science” as a self-paced course once they register for the course.
Certification Qualifying Details
Edureka shall provide a course certificate for Data Science with Python Certification Course to those participants who successfully complete the final project.
What you will learn
Knowledge of python
As the Data Science with Python Certification Course comes to an end, participants will have gained knowledge about the following:
- Learn the fundamentals of Python
- Understand how to create generic python scripts
- Acknowledge the concept of Machine Learning and types of machine learning
- Grasp the Basic Functionalities of a data object
- Understand the Supervised Learning Techniques and their implementation
- Expertise in performing factor analysis using PCA
- Learn about the various types of clustering used for analyzing the data
- Understand the Association rules and their extensions
The syllabus
Introduction to Data Science and ML using Python
Topics
Hands On/Demo
- Creating “Hello World” code
- Variables
- Demonstrating Conditional Statements
- Demonstrating Loops
Data Handling, Sequences and File Operations
Topics
- Data Analysis Pipeline
- What is Data Extraction?
- Types of Data
- Raw and Processed Data
- Data Wrangling
- Python files I/O Functions
- Numbers
- Strings and related operations
- Tuples and related operations
- Lists and related operations
- Dictionaries and related operations
- Sets and related operations
Hands On/Demo
- Tuple - properties, related operations, compared with the list
- List - properties, related operations
- Dictionary - properties, related operations
- Set - properties, related operations
Deep Dive – Functions, OOPs, Modules, Errors, and Exceptions
Topics
- Functions
- Function Parameters
- Global Variables
- Variable Scope and Returning Values
- Lambda Functions
- Object Oriented Concepts
- Standard Libraries
- Modules Used in Python
- The Import Statements
- Module Search Path
- Package Installation Ways
- Errors and Exception Handling
- Handling Multiple Exceptions
Deep Dive – Functions, OOPs, Modules, Errors and Exceptions
Hands On/Demo
- Lambda function in Python
- Errors and Exceptions in Python
- Packages and Modules in Python
- Functions - Syntax, Arguments, Keyword Arguments, Return Values
- Sorting - Sequences, Dictionaries, Limitations of Sorting
Introduction to NumPy, Pandas and Matplotlib
Topics
- Data Analysis
- NumPy - arrays
- Operations on arrays
- Indexing, slicing, and iterating
- Reading and writing arrays on files
- Pandas - data structures & index operations
- Reading and Writing data from Excel/CSV formats into Pandas
- Metadata for imported Datasets
- Matplotlib library
- Grids, axes, plots
- Markers, colors, fonts, and styling
- Types of plots - bar graphs, pie charts, histograms
- Contour plots
Hands On/Demo
- NumPy library - Creating NumPy array, operations performed on NumPy array
- Pandas library - Creating series and data frames, Importing and exporting data
- Matplotlib library - Using Scatterplot, histogram, bar graph, a pie chart to show information, Styling of Plot
Data Manipulation
Topics
- Basic Functionalities of a data object
- Merging of Data objects
- Concatenation of data objects
- Types of Joins on data objects
- Exploring and analyzing datasets
- Analysing a dataset
Hands On/Demo
- Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples(), GroupBy operations, Aggregation, Concatenation, Merging and joining
Introduction to Machine Learning with Python
Topics
- Revision (numpy, Pandas, scikit learn, matplotlib)
- What is Machine Learning?
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- Linear regression
- Gradient descent
Hands On/Demo
- Linear Regression – Boston Dataset
Supervised Learning - I
Topics
- What are Classification and its use cases?
- What is Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Perfect Decision Tree
- Confusion Matrix
- What is Random Forest?
Hands On/Demo
- Implementation of Logistic Regression, Decision Tree, Random Forest algorithms
Dimensionality Reduction
Topics
- Introduction to Dimensionality
- Why Dimensionality Reduction
- PCA
- Factor Analysis
- Scaling dimensional model
- LDA
Hands On/Demo
- Implementing PCA
- Scaling dimensional model
- Implementing LDA
Supervised Learning - II
Topics
- What is Naïve Bayes?
- How Naïve Bayes works?
- Implementing Naïve Bayes Classifier
- What is a Support Vector Machine?
- Illustrate how Support Vector Machine works
- Hyperparameter Optimization
- Grid Search vs. Random Search
- Implementation of Support Vector Machine for Classification
Hands On/Demo
- Implementation of Naïve Bayes, SVM algorithms
Unsupervised Learning
Topics
- What is Clustering & its Use Cases?
- What is K-means Clustering?
- How does K-means algorithm work?
- How to do optimal clustering
- What is C-means Clustering?
- What is Hierarchical Clustering?
- How Hierarchical Clustering works?
Hands On/Demo
- Implementing K-means Clustering
- Implementing Hierarchical Clustering
Association Rules Mining and Recommendation Systems
Topics
- What are Association Rules?
- Association Rule Parameters
- Calculating Association Rule Parameters
- Recommendation Engines
- How does Recommendation Engines work?
- Collaborative Filtering
- Content-Based Filtering
Hands On/Demo
- Implementing Apriori Algorithm
- Performing Market Basket Analysis
Reinforcement Learning
Topics
- What is Reinforcement Learning?
- Why Reinforcement Learning?
- Elements of Reinforcement Learning
- Exploration vs. Exploitation dilemma
- Epsilon Greedy Algorithm
- Markov Decision Process (MDP)
- Q values and V values
- Q – Learning
- Values
Hands On/Demo
- Calculating Reward
- Discounted Reward
- Calculating Optimal quantities
- Implementing Q Learning
- Setting up an Optimal Action
Time Series Analysis
Topics
- What is Time Series Analysis?
- Importance of TSA
- Components of TSA
- White Noise
- AR model
- MA model
- ARMA model
- ARIMA model
- Stationarity
- ACF & PACF
Hands On/Demo
- Checking Stationarity
- Converting non-stationary data to stationary
- Implementing Dickey-Fuller Test
- Plotting ACF and PACF
- Generating the ARIMA plot
Model Selection and Boosting
Topics
- What is Model Selection?
- The need for Model Selection
- Cross-Validation
- What is Boosting?
- How Boosting Algorithms work?
- Types of Boosting Algorithms
- Adaptive Boosting
Hands On/Demo
- Performing Cross Validation
- Implementing AdaBoost using Python
Statistical Foundations (Self-Paced)
Topics
- What is Exploratory Data Analysis?
- EDA Techniques
- EDA Classification
- Univariate Non-graphical EDA
- Univariate Graphical EDA
- Multivariate Non-graphical EDA
- Multivariate Graphical EDA
- Heat Maps
Hands-on
- mplementing Graphical EDA Techniques
- Implementing Non-Graphical EDA Techniques
Database Integration with Python (Self-Paced)
Topics
- Basics of database management
- Python MySql
- Create database
- Create a table
- Insert into table
- Select query
- Where clause
- OrderBy clause
- Delete query
- Drop table
- Update query
- Limit clause
- Join and Self-Join
- MongoDB (Unstructured)
- Insert_one query
- Insert_many query
- Update_one query
- Update_many query
- Create_index query
- Drop_index query
- Delete and drop collections
- Limit query
Hands-on
- CRUD operations using Python MySql and MongoDB
Data Connection and Visualization in Tableau (Self-Paced)
Topics
- Data Visualization
- Business Intelligence tools
- VizQL Technology
- Connect to data from the File
- Connect to data from the Database
- Basic Charts
- Chart Operations
- Combining Data
- Calculations
Hands-on
- Connecting to data from File, Database, and Server
- Performing operations on Hierarchies, Data Granularity and Highlighting feature
- Creating calculated fields using basic functions
- Defining LOD expressions
- Creating Parameters
- Performing User Input and What-if analysis
Advanced Visualizations (Self-Paced)
Topics
- Trend lines
- Reference lines
- Forecasting
- Clustering
- Geographic Maps
- Using charts effectively
- Dashboards
- Story Points
- Visual best practices
- Publish to Tableau Online
Hands-on
- Analyzing data using techniques including Forecasting, Trend Lines, Reference Lines, Clustering, and Geographic Maps
- Building Dashboard Layout and Formatting
- Building Story points
In-Class Project (Self-Paced)
Topics
- Predict the species of Plant
Hands-on
- Analyze the data
- Predict the plant species
Admission details
Filling the form
Interested candidates can observe the following steps to enrol for the Data Science with Python Certification Course:
Step 1: Click on the given official URL for the course https://www.edureka.co/data-science-python-certification-course
Step 2: Candidates have to select enrol now.
Step 3: Fill in the details asked for and select start learning
Step 4: Candidates are required to select their preferred batch before they can proceed for payment.
Step 5: Pay the course fees through the preferred method to complete the course enrollment.
How it helps
The Data Science with Python Certification Course will enable the participants to gain a detailed understanding of the concepts of data science using python. They will be skilled enough to build and use Machine learning applications and other scientific computations using python.
Python is becoming an important element for Data Analytics and a must for Professionals in the Data Analytics domain. Online learning mode will make it feasible for the participants to learn at their own pace. Students/ professionals can complete the course while meeting their time constraints.
This course will enable the participants to excel and further their career in this domain of the industry. It will not only develop their skills but also their resume. The course offerings will equip the participants with industry relevant skills which will in turn accelerate the career growth of the participants.
Real life case studies will provide the participants with hands-on experience. They will be given conceptual as well as practical knowledge which will make them industry ready. With their skills and knowledge, participants can apply for higher profile jobs at companies hiring certified professionals for data analytics. Participants of this course are likely to get an upper hand over other professionals of this domain.
FAQs
What if the participants are unable to attend any class?
If the participants are unable to attend a class, they have the option of attending the missed lecture through another batch or they can attend the recorded class that shall be available in their LMS.
Will Edureka provide placement assistance?
Participants will be given a resume builder tool in their LMS through which they will be able to create a good resume in 3 easy steps. They will be given unlimited access to templates for different roles and designations.
Can the participants attend a demo lecture?
Participants can view the sample class recording. It will give them a clear insight as to how the classes will be conducted, quality of instructors and the level of interaction in the classes.
Who will be the course instructors?
All the Edureka instructors are professionals from the Industry and subject matter experts. They have a minimum of 10-12 years of relevant IT experience. They are given training by Edureka to educate the participants better.
What if the participants have more queries?
If the participants have any queries, they can contact the officials at +91 98702 76459/1844 230 6365 (US Toll-Free Number) or drop a mail at sales@edureka.co
How will the practicals be executed?
Participants can do their assignments/case studies using Jupyter Notebook. It will be installed on their Cloud Lab environment, access details of which will be given on their LMS. They will be able to access their Cloud Lab environment from a browser.
What is the duration of the course?
Data Science with Python Certification Course by Edureka includes 42 hours of Online Live Instructor-led Classes.
What is the purpose of CloudLab?
CloudLab is a pre-installed cloud-based Jupyter Notebook with Python packages on the cloud-lab environment. It is provided by Edureka as a part of the Python Certification Course in which the participants can execute in-class demos and work on real-life projects.
How many case studies are included in the course?
This course includes 40 case studies which will enhance the learning experience of the participants. They will also be given 4 Projects to improve their implementation skills.
How are the batches divided?
The batches are divided as per weekend and week day classes. The weekend batch includes 14 sessions of 3 hours each and Weekday batch includes 21 sessions of 2 hours.
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