Tej Data Analytics for developers

These days, as the world is getting more and more connected through different types of digital devices, a massive volume of data is getting emanated from a huge number of digital sources. Businesses and organizations from across the globe are leveraging the power of this data and putting it to their advantages.

The size and number of available data sets has grown rapidly as data is collected by devices such as mobile devices, cheap and numerous information-sensing Internet of things devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 exabytes of data are generated. Based on an IDC report prediction, the global data volume was predicted to grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. By 2025, IDC predicts there will be 163 zettabytes of data. One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.

Wow! A lot of data out there! There is no doubt that there will always be demand for data specialist.

If you are just starting out your career in data technology or thinking about a career change, please follow these paths:

1) To start with, you need solid SQL knowledge. Please use the link here.
2) Python basics : Please use these links in my tutorials. Python basics 1 and Python basics 2

For the intermediate level, please follow these paths:

1) Apache Spark: The tutorials here will show you how to write similar codes in Pyspark, pandas and SparkSQL. Please use these links in my tutorials. PySpark pandas SparkSQL 1 and PySpark pandas SparkSQL 2
2) Machine Learning: To get a solid understanding of machine learning algorithms, please use these links:

Linear regression
Polynomial regression
Multinomial logistic regression
Binary logistic regression
DecisionTree
KFold-Cross-Validation
K-Means Clustering

3) Python SQL Server : For Python Scripting in SQL Server Management studio, please use this link: Python with SQL Server
4) Azure Databricks: If you want to learn how to manipulate data in Azure Databricks, use this link Azure Databricks

For Experienced Data Engineer:

1) Advanced projects: If you have solid understanding of Machine Learning using Apache Spark (PySpark and Spark SQL), you might want to practice this project.
More links will be available later for advanced projects.

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