In the Past Decade, Python and R programming become most preferred sowtware languages for Data Science and it considered as more powerful and flexible platforms for building DataScience systems with Subject Matter Codes and Algorithms. Python and R Programming with Data Analytics is designed to provide in depth knowledge on data Prediction and its techniques.
Data science is a combination of specific scientific methods, processes, algorithms, programs and systems to extract knowledge and gain accurate outcome from various forms of data.
Data science is a structure "to practice or science of collecting and analyzing large quantity of data, data analysis, machine learning and their related methods" in order to "understand and analyze actual fact" with data. It state, techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.
In the domain of data science, we are concerned with solving a problem or answering a question through analyzing data
Often, we construct a model of some sort to predict outcomes or discover underlying patterns > predictive & descriptive models,
Goal of modeling is to gain insights from which we can formulate actions to influence future outcomes or behaviors.
For our purpose, we need a data science methodology to guide us in achieving our organization goals.
Data Science using the scientific method -The Scientific method as a on-going process
- Develop General Theories
- Think of Interesting Questions
- Formulate hypotheses
- Develop Testable Predictions
- Gather Data to test predictions
- Refine, Alter, Expand or Reject Hypotheses
- Using R/Python Programming + Data Analytics Tools + SQL + Business Aspects + Tableau Software + Machine Learning Algorithms
- Students will develop relevant programming abilities
- ITechGurus training course is mainly focussed on Python for core data science programing, it also includes R as necessary to enable professionals working in R
- Demonstrate proficiency with statistical analysis of data
- Develop the ability to build and assess data-based models
- Execute statistical analyses with professional statistical software
- Demonstrate skill in data management
- Apply data science concepts and methods to solve problems in real-world contexts and will communicate these solutions effectively
- This course allows candidates to obtain an in-depth knowledge by laying a strong foundation and covering all the latest data science topics.
- Professional Responsibilities and ethics
- Increase your opportunities for career development and for increased earnings
Python libraries for data Analysis
Standard Python libraries useful for Data Analysis with Python are:
- Pandas - for data munging and preparation
- NumPy - abundance of useful features for operations on n-arrays and matrices in Python
- SciPy - library of software for engineering and science
- Matplotlib - tailored for the generation of simple and powerful visualizations
- Statsmodels - data exploration by using methods of estimation of statistical models
- scikit-learn - concise & consistent interface to the common ML algorithms
- Seaborn - visualization of statistical models; based & highly dependent on Matplotlib
- Bokeh - is aimed at interactive visualizations; independent of Matplotlib - main focus is interactivity, with presentation via modern browsers in the style of Data-Driven Documents
- Plotly - web-based toolbox for building visualizations, exposing APIs to Python, etc..
- Theano / TensorFlow / Keras
- NLTK - Natural Language Toolkit - tasks of symbolic & statistical NL processing
- Gensim / Scrapy
- Dataiku’s Data Science Studio
Notebooks & Workbenches for Data
- Jupyter Notebook (formerly IPython)
- Apache Zeppelin
- BeakerX (Beaker Notebook)
- IBM Watson Studio - a platform for data scientists
- Databricks Unified Analytics Platform
- Cloudera Data Science Workbench
- MapR Data Science Refinery
- Dataiku’s Data Science Studio
|Bachelor Degree (or) Master Degree (or) Diploma (or) Global Equivalent
|| Not Required
Candidates who want to be a Data Scientist, Big Data Analyst, Analytics Manager/Professionals, Business Analyst, Software Developer, Programmers, Team leaders, Business Analyst, Data Base Analyst and executives.
- Graduates who are looking to build a career in Data Science and Machine Learning
- Employees – Organization is planning to shift to Big data tools
Mid-level Executives, Managers with Knowledge of basic programming
CDS® (Certified DataScientist) is a globally recognized Certification offered by BRAINskills. BRAINskills is only an authorization body certifying institutes as REP (Registered Education Partners) , who are aligned with BRAINskills Training Curriculum. These institutes are audited by BRAINskills™ through onsite audit to ensure the Training requirements and knowledge of trainers.
- Multiple Choice
- 60 questions per exam
- One mark awarded for every right answer
- No negative marks for wrong answers
- 120 minutes duration
- Proctored online exam