Do you wonder programming in Python for data analytics and data science? Learn why and how Python is the best programming language for statistical analysis, data mining and visualization from our training.
Why Data Scientists Prefer Python?
Organizations are maintaining large amounts of user data. Hence the need for Data Science department.
Data science is the study of data - collection, organization, analysis, and presentation to effectively extract useful information.
It is an interdisciplinary field consisting of more than one branch of studies such as mathematics, statistics, computer science, information science, and machine learning to gain understandings from both structured and unstructured data.
Data scientist - data engineer, machine learning expert and business analyst, is a skilled professional responsible for using most powerful hardware, programming systems, and efficient algorithms to explore the data, do data cleaning and visualization, modeling and make insightful reports that could help drive high-stake decisions.
These data scientists need an easy-to-use language that has decent library availability and great community participation to perform hassle-free tasks. That’s why Python is so prevalent in the booming data science industry.
It is easy, open-source and has simple syntax. This enables programmers to roll out programs and get prototypes running making the development process much faster.
This general-purpose programming language also provides innumerable libraries that support data cleaning, data munging, data visualization, and machine learning. Top 10 Python libraries for Data science are – Numpy, Pandas, Matplotlib, SciPy, TensorFlow, Simpy,Keras, Seaborn, NLTK, and Scikit-Learn.
Python is in the mode of continuous development, meaning it receives regular updates on libraries and releases. So, learning Python for data science is time well spent.
Data Scientist with Python has been ranked as the number one job (source: Glassdoor). The average salary of a data scientist with Python skills is $120,000 in the USA(source: indeed.com).
Best Python for Data Science Training
Are you ready to take a big step towards becoming a Data Scientist?
Learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms will be the best bet.
- Tech giant Google has created a deep learning framework called TensorFlow – Python is the primary language used for creating this framework.
- It has its footprint in Netflix.
- Production engineers at Facebook long been using Python as a prominent language.
- It integrates well with the infrastructure-as-a-service as well as platform-as-a-service cloud providers.
- It is a multi-paradigm programming language - it supports Object-oriented programming, structured programming, functional programming, and aspect-oriented programming.
Python for Data Science course at Online Idea Lab enables you to learn
- Python fundamentals and advanced concepts - data operations, file operations, object-oriented programming
- how to code in Jupiter Notebooks
- data science concepts from scratch
- Python libraries such as Pandas, Numpy, Matplotlib, and more which are essential for Data Science
This training on Python for data analysis is packed with real-life analytical challenges which we will solve together. Our course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course!
We extend our support to
- beginners with some Python programming experience
- experienced developers looking to make a career in data science
- business analysts
- data engineers
- managers or business executives
- anyone who wants to learn data science
It is good to have
- basic programming skills or knowledge of python programming
- strong mathematics or statistics knowledge is highly appreciable
Everything About Data!
Data scientists need to be innovative, analyze and manage large amounts of data, develop operational models, create visualizations and present statistical reports.
When data analysis tasks involve integration with web apps or when there is a need to incorporate statistical code into the production database, Python is the best programming language due to its offerings like extensive libraries, modules, and packages.
Huge Job Demand
As the Data Science field is growing, there is an increase in demand for data engineers, analysts or statisticians with Python knowledge. Such certified and experienced professionals are paid high salaries across the industry.
Python has a rich ecosystem. More and more volunteers are creating data science libraries that provide easy access for aspirants who want to find solutions to their coding problems.
Check Your Python Data Science Knowledge
Main Modules of Python Data Science Course
This course teaches Python libraries, modules, packages and toolkits used in Data Science field.
Data Science Overview
- Introduction to Data Science
- Different Sectors Using Data Science
- Purpose and Components of Python
- Data Science LifeCycle
- Introduction to Recommender systems
- Apache Mahout Overview
- Data Acquisition
- Descriptive statistics
- Combining data frames
- Discretization and Binning
- String manipulation
Data Analytics Overview
- Data Analytics Process
- Exploratory Data Analysis(EDA)
- EDA-Quantitative Technique
- EDA - Graphical Technique
- Data Analytics Conclusion or Predictions
- Data Analytics Communication
- Data Types and Plotting
Statistical Analysis and Business Applications
- Introduction to Statistics
- Statistical and Non-statistical Analysis
- Data Processing Operation
- Data distribution
- Measures of Central Tendency
- Hypothesis Testing
Python Environment Setup and Essentials
- Data Types with Python
- Operators and Functions
- OOPs, Classes and Objects
- Multithreading and Data Structures
- Lambda expressions
- Connecting with the database to pull the data
Mathematical Computing with Python (NumPy)
- Introduction to mathematical computing in Python
- What are arrays and matrices, array indexing, array math, ND array object
- Creation, Printing Arrays
- Basic Operations- Indexing, Slicing, and Iterating, copy and view
- Shape Manipulation - Changing shape, stacking and splitting of array
- Vector stacking
- Data types, standard deviation
- Conditional probability in NumPy, correlation, covariance
Scientific Computing - SciPy
- Introduction to SciPy, What are the characteristics of SciPy
- Building on top of NumPy
- SciPy Sub Package - Integration and Optimization
- SciPy Sub Package - Statistics, Weave, and IO
- Various sub-packages for SciPy like Signal, Integrate, Fftpack, Cluster, Optimize, Stats and more
- Bayes Theorem with SciPy
Data Manipulation with Pandas
- Introduction to Pandas
- Importing data into Python
- Understanding DataFrame
- Indexing Data Frames
- View and Select Data Demo
- Missing Values
- Data Operations
- Renaming Columns
- File Read and Write Support
- Pandas Sql Operation
Data Visualization - Matplotlib
- Introduction to Data Visualization
- Types of Plots, plot() -(x,y), Plot and Subplots
- Controlling Line Properties
- Working with Multiple Figures
- Histograms and Scatter Plots
- Charts- Line, Bar, Pie
- Introduction to Sklearn library
- Stats models
- Demo - Basics of visualization with ggplot & Seaborn, Inferential visualization with Seaborn, Visual summary of different data combinations
Time Series Forecasting
- Understand Time Series Data
- Visualizing Time Series Components
- Exponential Smoothing
- Holt's Model
- Holt-Winter's Model
- ACF & PACF
- Case Study: Time Series Modeling on Stock Price, Implementing Dickey-Fuller Test
Web Scraping with BeautifulSoup
- Web Scraping and Parsing
- Understanding and Searching the Tree
- Navigating options
- Modifying the Tree
- Parsing and Printing the Document
- Learn about ScraPy - for extract data using APIs or as a general-purpose web crawler
Python integration with Big Data
- Why Big Data Solutions are Provided for Python?
- Hadoop Core Components, Python Integration with HDFS using Hadoop Streaming
- R introduction, How R is typically used
Features of R, R+Hadoop
- Python Integration with Spark using PySpark
- Introduction to PySpark Shell
- Submitting PySpark Job, Spark Web UI
- Data Ingestion using Sqoop
- DataFrames and Spark SQL
- Understanding Apache Kafka and Apache Flume
- Apache Spark Streaming - Processing, Multiple Batches
- Machine Learning using Spark Mllib
Still Thinking? Join our Demo Sessions
Ask for a free demo class before you make up your mind.
Our Python Courses
Bulk Booking Discounts
Please choose if you want to do multiple courses together or would like to bring in your colleagues with you.
Specific Needs? Let's Customize!
Please choose if your organization has a specific training agenda, we can customize a course for you.
Why Online Idea Lab?
Operate in Broader Programming World
As a data scientist, you can find new sources of data and new ways to analyze it when you are hands-on in general programming like Python.
Do Everything Under One Roof
You will be able to do econometrics, GIS work, network analysis, data manipulation using Python data science and machine learning libraries.
Various Career Options
After completing our Python for data science course you can apply for a data engineer, data analyst, business analyst, data scientist, or programmer role.
Curriculum Designed by Experts
Our courseware is designed by industry expert trianers who have real-world experience. Get the most advanced knowledge and skills by interacting with them - listen, learn, question, and apply.
Master Python Programming
Learn components of data science from scratch using Python libraries by working on real-time projects, case studies, sample examples, coding exercises, and industrial assignments.
Talk to trainer
Talk directly with our trainer to get detailed information about this course.
The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decade.
We are always keen to know what our students have to say about their experience at Online Idea Lab. Read below and know what they have shared.
A total hands down!!! I rate their teaching, tech support, and quality of education as 5/5. I took Python Data Science course and was amazed by the coaching and practical labs they provide to students. This is by far one-stop solution for your studies.
Saravana Kumar, Software Engineer
I am in data science field for two years now. I knew I have to upgrade my programming skills and what better than Python. This institute provides the best training. The mentors are well presentable and dedicated. They have gone extra mile to provide a deep understanding of the language. I truly recommend this institute.
Ciara Patridge, Data Scientist
I was expecting a lot of theory in this training but was really happy to see continuous practical labs by the end of each module. The trainer was very knowledgeable and helpful. I am now all set to handle tougher real-world situations at workplace.
Abhin Saha, Software Developer
A clean, simple and fantastic learning method followed here puts you in ease to understand tougher concepts and learn of Python for Data Science. The course curriculum is well structured and trainers are experienced. This combination works great. Cheers.
Pablo Mario, Data Engineer
Python for data science is the right choice to deep-dive into data science concepts. Get in touch with us to know more.
How to proceed?
Few Interesting Facts and Stats
- Python lives in the heart of data science.
- In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
- Top libraries for data science are - Numpy, Pandas, Keras, seaborn, scikit-learn, matplotlib, stat models, scipy, simpy etc.
- The Economist claimed in 2018 that Python is becoming the world’s most popular coding language.
- Companies like Industrial Light and Magic, Spotify, Quora, Netflix, Dropbox, and Reddit all rely on Python.
Companies Hiring Python Professionals