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The Path to Becoming a Successful Data Analyst

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The adoption of data science is growing phenomenally across the industry. As organizations are realizing the importance of data-driven decisions, data science professionals of all levels are sought by companies. Data analysts and data scientists are on top of the list of roles that companies are seeking to fill fast. 

 Data analysts help companies make decisions by analyzing data available with companies. For this, they are paid handsomely. A data analyst, however, needs a gamut of skills to perform their work efficiently. The breadth of their skills includes programming, statistics, mathematics, and a fair bit of machine learning. 

 Skills required for data analysts 

 A data analyst requires a broad spectrum of skills. On one end, the skills required for a data analyst include statistics and higher mathematics. On the other end, programming skills converge with software development. Each skill contributes significantly to various tasks and missing out on one even one skill is not an option.  

 Programming skills  

 R and Python are two most commonly used programming languages for data analysts. R is an old language and has been used primarily for statistical analysis. Python is a comparatively new language and is known for easy-to-learn and easy-to-use in the analytics community. Many analytics professional prefer to use R due to their previous experience. New entrants, however, choose Python due to its widespread application and increasing domination in the community.  

 Programming is required to perform all tasks related to data analytics— data collection, manipulation, analysis, and even modeling. In R, Diplyr, ggplot2, and reshape2 are major packages that data analysts need to learn. Diplyr converts into SQL, while ggplot2 and reshape2 are useful to create graphs from data. In Python, NumPy, Pandas, Matplotlib, Scipy, Scikit-learn, and Seaborn are important packages to learn. These packages enable all data analytics-related techniques. 

 Statistics 

 Programming can be called enabler of data analytics. Statistics is the foundation of data analytics. All types of data analytics –prescriptive, descriptive, diagnostic, and predictive analytics are based on statistical techniques. Any type of analytical technique performed on data is based on statistical concepts and techniques. Right from describing data (descriptive analytics) to building predictive models (predictive analytics), data analytics uses statistical concepts. 

 Concepts such as measures of central tendency—mean, median, mode; measures of variability – variance, standard deviation, Z-square, R-square values; measures of relationship between two variables – correlation, covariance, and more are important to perform various analytical techniques.  

 Machine learning 

 At an associate level, knowledge of machine learning is generally not required and data analysts spend most of their time on data collection and analysis. At senior levels, however, knowledge of machine learning is expected to build models. Learning machine learning for data analysts can be considered as the next big push in their Big Data career

  A data analyst majorly needs to know about the following three machine learning skills. 

  1. Supervised learning – In this, an algorithm works in two phases: learning and test. The algorithm will first learn and then apply it in the future. Algorithms such as logistic regression, decision trees, support vector machines, and Naïve Byes classification are essential to master supervised learning.  
  1. Unsupervised learning- This used when there are multiple relationships among several variables. In the end, a model suggests recommendations. Facebook friends’ suggestion is a good application of unsupervised machine learning. Principal Component Analysis, Singular Value Decomposition, Clustering algorithms, and Independent Component Analysis, are frequently used algorithms for unsupervised machine learning.   
  1. Reinforced learning – This is the space between supervised and unsupervised machine learning. TD- learning, Q-learning, and genetic algorithms are frequently used for reinforced learning.  

Build a portfolio

Consistent practice and working on various industry projects help to get hang of the skills. A portfolio of projects attracts employers’ attention. Thus, working on projects is mandatory to break into the industry as soon as you complete learning the skills. Kaggle and Data-Driven are platforms where analysts can work on real industry projects. Working on different analytics including analysis, visualization, predictive modeling and more expand the portfolio and increases job opportunities.  Additionally, getting a data analytics certification also helps to get validation for the skills and be more credible in the eyes of employers.

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